Education and the Gender Wage Gap

Education and the Gender Wage Gap

   Introduction

 Better opportunities, higher income, a larger wealth of knowledge… These are just a few of the many reasons that 19 million Americans enroll in college annually. Women make up about 56 percent of that nineteen million, and that number is rising. So why is it that the number of women enrolling in college is increasing? It could be the fact that women are now more able to pursue careers that historically have been male dominated, such as science, engineering, and law. It could also be that simply men are more likely to work in jobs that do not require a college diploma, such as manual labor jobs like manufacturing and construction. With more women pursuing higher education, the question remains: does obtaining higher education in fact close the gender wage gap?

   Literary Review

 In 2016, the wage gap disparity between men and women as a whole narrowed to 81 cents on the dollar earned by a man, up from 57 cents on the dollar in 1975 (“Women Can’t Win). As the age gap has become smaller and smaller, the number of women completing Associate’s, Bachelor’s, and Doctoral Degrees has risen. From 1970, the number of bachelor’s degrees obtained by women has increased from 43 percent to 57 percent in 2015 (“Women Can’t Win”). Education has served as the main way for women to catch up to the earnings of men. Interestingly enough, the largest wage gaps exist in the highest paying fields. Over a lifetime, out of men and women who earn graduate degrees in business, women earn $1.6 million less over the course of their careers (“Women Can’t Win”). One reason for this may be that women still take on the majority of caretaking for their children. Studies show that most widening in the wage gap occurs to college graduates in the first seven years after leaving college because that is when families are being formed and mothers must take time away from work (Goldin). As women’s family responsibility increases, women’s advances and wages fall farther behind.

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There are many factors to consider beyond just level of education, but according to the article, “The Gender Income Gap and the Role of Education,” there are four major factors that impact the gender income gap: choice of college major, skills measured by standardized tests, amount of education, and selectivity of the college attended. When considering these factors, it is important to remember that many college majors still show segregation (Bobbitt-Zeher). Women are often much more likely than men to major in fields that are less rewarded with higher incomes like childhood education and the humanities. In 2000-2001, women earned 20 percent of engineering degrees and 77 percent of education degrees (Bobbitt-Zeher).

When looking at skills measured by standardized tests, research has suggested that since the 1970’s, higher abilities and scores in STEM related majors have been predictive of higher future salaries. Male students have been dominant on these standardized tests, but the disparities between men and women are shrinking. One study found that the wage gap disappears between men and women with the highest math skills (Bobbitt-Zeher).

College selection can also play an important role in job opportunities, often the more prestigious the institution, the higher salaries expected in the future. Studies show that more prestigious and selective universities are less likely to offer degrees that are typically female dominated like childhood education, so depending on a female’s major choice, she may attend a less selective institution.

  The differences in the college majors chosen by males and females is critical to understanding the wage gap. More and more research suggest that women are more sensitive to negative feedback than men (Kugler). This research examines the likelihood that a woman would change majors in response to bad grades. In the article, “Choice of Majors: Are Women Really Different from Men?” their data shows twelve percent of students change majors, and of those that switch, sixty percent are female. The study finds that it does in fact take more than just negative feedback for a female to switch majors, but also low grades, gender composition of class, and external stereotyping signals (Kugler). When considering STEM majors that are more challenging, males may be more likely to persist and graduate than that of female peers.

 After considering median lifetime earnings based on eight different education levels, it is evident women earn about a quarter less than men over a lifetime (Carnevale). As earning levels for men increase, women have to obtain more college to keep up. According to the data provided in the article, “The College Payoff: Education, Occupations, Lifetime Earnings,” men with some college but no degree earn about the same as a woman with a Bachelor’s Degree. Interestingly, Claudia Goldin, a Harvard economist, suggests that men and women with identical degrees and experience are still paid unequally (“Women Can’t Win”).

   Data

 My dependent variable, wage, comes from the Bureau of Labor Statistics article, “More Education Still Means More Pay in 2014.” The study spans 35 years and compares median weekly earnings for men and women with varying levels of education. My first independent variable is gender, taken from the same article, one representing male and two representing females. My second independent variable is education from the same article. The levels of education represented are .25 representing less than a high school diploma, .5 representing a high school diploma, .75 representing some college but no degree, and 1.0 representing a bachelor’s degree. In my study, I included a total of 72 observations of weekly wages by education and gender from years 1979-2014.

Variables

Observations

Mean

Standard Deviation

Minimum

Maximum

Wages

72

773.2777778

260.0615432

404

1465

Gender

72

1.5

0.503508815

1

2

Education

72

0.614583333

0.278127225

.25

1

Dependent Variable: Wages

 

(OLS) Unrestricted

(OLS) Restricted

Constant

638.618***

533.154***

 

(35.2748)

(57.5992)

Gender

-240.428***

-204.792***

 

(18.7826)

(31.2670)

Education

805.913***

913.578***

 

(34.0031)

(58.3530)

Nobs

72

30

R2

0.909113

0.911608

  Note: Standard errors are in parentheses

Regression Specification:

Wages = 638.618 – 240.428 Gender + 805.913 Education + u

  Wages = 533.154 – 204.792 Gender + 913.578 Education + u

    Interpretation

I performed two separate regressions, one unrestricted, and one restricted to the years 2000-2014. In the unrestricted model, the coefficient gender shows that a one-unit change in gender (male to female), if education remains the same, causes a 240.428 decrease in wages. The coefficient education shows that a one-unit change in education, if gender remains the same, causes an increase in median weekly wages by 805.913. In the restricted model, the coefficient gender shows that a one-unit change in gender (male to female), if education remains the same, causes a 204.792 decrease in wages. The coefficient education shows that a one-unit change in education, if gender remains the same, causes an increase in median weekly wages by 913.578

When comparing the results of these two regressions, it is interesting to see that the margin for median weekly wages for females vs. males actually became smaller when looking at years 2000-2014 compared to 1979-2014 (approximately $35.636 less per week). It is also interesting to see that completing more education raises median weekly wages substantially in 2000-2014 compared to 1979-2014 (approximately $107.665 more per week).

 A t-test of the OLS unrestricted regression was conducted to test if gender has an effect on wages. The null hypothesis is: Gender does not have an effect on wages. The alternate hypothesis is: Gender does have an effect on wages. The Tcrit = +/-1.9935 at a 95% confidence level. The Tstat = -240.428/18.7826 = -12.8005707 and therefore, since the Tstat of gender is larger than the Tcrit, gender has a statistically significant effect on wages. The null hypothesis is rejected. Another t-test was conducted to see if education has an effect on wages. The null hypothesis is that education does not have an effect on wages. The alternate hypothesis is that education does have an effect on wages. The Tcrit remains the same. The Tstat = 805.913/34.0031 = 23.7011625. Since the Tstat is greater than the Tcrit, education also has a statistically significant effect on wages. The null hypothesis is rejected.

A White’s test was conducted on the unrestricted specification of the OLS regression to determine if heteroskedasticity is present in the regression model. The null hypothesis is: Heteroskedasticity in not present. The alternate hypothesis is: Heteroskedasticity is present. Because the p-value of 0.050049602 is greater than the p-value of 0.05, we fail to reject the null hypothesis and heteroskedasticity is not present at a 95% confidence interval.

    Conclusion

 After completing this study, it is clear there are many factors that affect the gender wage gap. That being said, there are many ways that this research can be expanded upon. One way would be recording more data on the wages of men and women with similar experience, test scores, family, and education levels. This data could provide more information on where the wage differences exist for men and women, and how big that gap is, if any, for men and women with similar factors. More research can also be done to study college major choices of men and women to see if the number of women choosing STEM majors has shifted. As more women continue to pursue degrees that will result in higher paying careers, research should be done to see if the wage gap decreases. The amount of information that already exists on this topic is huge. The important thing will be to continue to collect this information as society advances. Understanding more of why and where the gender wage gap exists will bring us closer to equality.

Works Cited

Bobbitt-Zeher, Donna. “The gender income gap and the role of education.” Sociology of education 80.1 (2007): 1-22.

Bureau of Labor Statistics, U.S. Department of Labor, The Economics Daily, More education still means more pay in 2014 on the Internet at https://www.bls.gov/opub/ted/2015/more-education-still-means-more-pay-in-2014.htm (visited November 27, 2018).

Carnevale, Anthony P., Nicole Smith, and Artem Gulish. “Women Can’t Win: Despite Making Educational Gains and Pursuing High-Wage Majors, Women Still Earn Less than Men.” (2018).

Carnevale, Anthony P., Stephen J. Rose, and Ban Cheah. “The college payoff: Education, occupations, lifetime earnings.” (2013).

Goldin, Claudia, et al. “The expanding gender earnings gap: evidence from the LEHD-2000 census.” American Economic Review 107.5 (2017): 110-14.

Kugler, Adriana D., Catherine H. Tinsley, and Olga Ukhaneva. “Choice of Majors: Are Women Really Different from Men? No.” w23735. National Bureau of Economic Research, 2017.

Year

Wages

Gender

Education

1979

1204

1

1

1979

562

2

0.5

1980

732

1

0.25

1980

795

2

1

1981

890

1

0.5

1981

638

2

0.75

1982

969

1

0.75

1982

434

2

0.25

1983

1172

1

1

1983

557

2

0.5

1984

668

1

0.25

1984

662

2

0.75

1985

1239

1

1

1985

666

2

0.75

1986

662

1

0.25

1986

899

2

1

1987

992

1

0.75

1987

427

2

0.25

1988

1306

1

1

1988

573

2

0.5

1989

954

1

0.75

1989

426

2

0.25

1990

953

1

0.75

1990

940

2

1

1991

592

1

0.25

1991

693

2

0.75

1992

782

1

0.5

1992

673

2

0.75

1993

574

1

0.25

1993

985

2

1

1994

927

1

0.75

1994

555

2

0.5

1995

782

1

0.5

1995

659

2

0.75

1996

537

1

0.25

1996

549

2

0.5

1997

1318

1

1

1997

404

2

0.25

1998

933

1

0.75

1998

1026

2

1

1999

824

1

0.5

1999

575

2

0.5

2000

950

1

0.75

2000

418

2

0.25

2001

1426

1

1

2001

695

2

0.75

2002

812

1

0.5

2002

428

2

0.25

2003

552

1

0.25

2003

1071

2

1

2004

954

1

0.75

2004

612

2

0.5

2005

790

1

0.5

2005

712

2

0.75

2006

550

1

0.25

2006

1062

2

1

2007

925

1

0.75

2007

421

2

0.25

2008

780

1

0.5

2008

691

2

0.75

2009

1465

1

1

2009

422

2

0.25

2010

917

1

0.75

2010

1071

2

1

2011

514

1

0.25

2011

679

2

0.75

2012

758

1

0.5

2012

578

2

0.5

2013

508

1

0.25

2013

407

2

0.25

2014

751

1

0.5

2014

1049

2

1

NOBS

72

72

72

Mean

773.2777778

1.5

0.614583333

St. Dev.

260.0615432

0.503508815

0.278127225

Minimum

404

1

0.25

Maximum

1465

2

1