Mierzejewski1
Gender Wage Gap for Financial Analysts: Individual Project
By: Megan Mierzejewski
Women and the Economy
University of Akron
Spring 2017
Professor: Amanda Weinstein
Introduction
The purpose of this paper will be to analyze the gender wage gap for Financial Analysts in 2015 using data for Ohio provided by IPUMS. Given that Financial Analysis requires a significant amount of math and computer skill along with verbal expression it is not surprising that this is a male dominated field ("13-2051.00 – Financial Analysts.", 2016).Because women are less encouraged to pursue further education or careers in higher paying fields such as math, finance, engineering, or the sciences, they become highly underrepresented and this division helps to widen the wage gap by enforcing the occupational segregation that leads to this underrepresentation. Women are also generally stereotyped as being averse to confrontation and more submissive than their male counterparts, meaning that they would be less likely to advance in careers that require a more dominant or aggressive personality.The results will be determined using a regression technique in Excel for 3 variables (years of schooling, married, and white) for each gender, and the unadjusted and adjusted wage gaps will be calculated as a means of determining the presence and severity of the wage gap.
Literature Review
In 2010, Glenn Ellison and Ashley Swanson researched the effects of math tests of secondary education students on the gender wage gap using scores from the American Mathematics Competition as the basis for ratings. The results indicate that there is a slight wage gap between students with average and below-average skills, but the gap is much larger among high math achievers and only continues to widen with increasing math skills. The authors believe that this is in large part due to the lack females in technical and science-related fields and may relate to the underrepresentation of women in math and science. The authors present alternative explanations for this wage gap such as the possibility that there is “less variance in the ability in the female population” or that the girls with strong math abilities also tend to have strong abilities in other areas and tend to pursue less math-focused paths in exchange for developing skills that would be more valuable in the long run. Despite the results, the authors remain optimistic that it may not be too difficult to, over time, increase the number of girls who succeed in math (Ellison and Swanson, 2010).
Another study in 2010 by Marianne Bertrand, Claudia Goldin, and Lawrence F. Katz explored the gender differences in the career dynamics among professionals in the business and financial sectors with their MBA (master’s degree in business administration) from 1990 to 2006. The authors noticed that these career programs had almost identical labor income and hours worked, but the gender gap had still been increasing considerably as the careers progressed. The authors present many explanations for why women are not progressing as strongly in these sectors. The first is that women have little preferences for highly-competitive careers and are less likely to aggressively negotiate for more equal pay. The second is that there is a small training advantage to male MBA students and an increasing in labor market returns. Another is gender differences in continuity and female workers suffering from a large loss of earnings due to increased likelihood of career interruptions. And lastly, are gender differences in labor hours worked, particularly due to the impact of children and the corresponding decrease in market experience. The authors promote the idea that, in fields that have a larger female population, there are economic benefits to re-organizing work to reduce the effect of career interruptions or allow for flexible labor options (Bertrand, Goldin, and Katz, 2010).
Data and Results
The data used in these regressions are from IPUMS for 2015. The career used was Financial Analyst (identification number 132051) and had a total sample size of 53 individuals (26 females and 27 males). Using Excel, the data was organized and a regression was conducted on the effects of years of education, whether or not the individual was married, and whether or not the individual was white in regards to hourly wage. One regression was run for male employees, another for female employees with the results shown below
Table 1. Female Model Regression
Regression StatisticsMultiple R / 0.21170051
R Square / 0.044817106
Adjusted R Square / -0.08543511
Standard Error / 14.33710658
Observations / 26
Coefficients / Standard Error / t Stat / P-value
Intercept / 8.16070558 / 35.78904674 / 0.228022435 / 0.821734777
School / 1.173039892 / 1.918489762 / 0.611439224 / 0.547175707
Married / 1.407084627 / 6.393183423 / 0.22009139 / 0.827829475
White / 5.598414888 / 6.785998345 / 0.824995027 / 0.418230423
Female Descriptive Statistics
Wage / School / Married / White
Mean / 34.70038 / 18.07692 / 0.730769 / 0.769231
Standard Error / 2.698814 / 0.298319 / 0.088712 / 0.084265
Median / 33.25 / 18 / 1 / 1
Table 2. Male Model Regression
Regression StatisticsMultiple R / 0.476595587
R Square / 0.227143353
Adjusted R Square / 0.126335965
Standard Error / 40.21653298
Observations / 27
Coefficients / Standard Error / t Stat / P-value
Intercept / -78.0912964 / 81.71134744 / -0.9557 / 0.349163
School / 6.4697955 / 4.54768237 / 1.422658 / 0.168258
Mar / 34.40276531 / 16.46568122 / 2.089362 / 0.047931
White / -15.2052243 / 22.46087507 / -0.67696 / 0.505181
Male Descriptive Statistics
Mean / 49.78533
Standard Error / 8.280385
Median / 35.6
Table 3. Both Gender Regression Model
Regression StatisticsMultiple R / 0.408239
R Square / 0.166659
Adjusted R Square / 0.097214
Standard Error / 31.14743
Observations / 53
Coefficients / Standard Error / t Stat / P-value
Intercept / -42.5661 / 48.59045 / -0.87602 / 0.385384
Gender / 16.02753 / 8.635522 / 1.856 / 0.069598
School / 3.593326 / 2.631734 / 1.365383 / 0.178498
Marr / 19.54444 / 9.349589 / 2.090407 / 0.041902
White / -2.56397 / 11.00561 / -0.23297 / 0.816776
Both Gender Descriptive Statistics
Mean / 42.38517
Standard Error / 4.502896
Median / 34
Adjusted Wage Model:
Wagei = -42.566 + 16.028male + 3.593school + 19.544marr – 2.564white
(m-Gender Coefficient) / m
(49.79 – 16.03) / 49.79 = 0.67804
Explained and Unexplained:
m = -78.09 + 6.47schoolm + 34.40marrm – 15.21whitem = 49.79
f= 8.16 + 1.17schoolf + 1.41marrf + 5.60whitef = 34.7
Wf* = -78.09 + 6.47schoolf + 34.40marrf – 15.21whitef = 52.28
Gender Wage Differential = Difference due to Qualification + Unexplained Difference
m - f = m - f+m - f
15.09 = -2.49 + 17.58
Table 4. Wage Statistics
Mean Wage / Adjusted Wage / Unadjusted Wage Gap / Unadjusted Wage DifferentialFemale / Male / Both
34.7 / 49.79 / 42.39 / 0.678 / 0.6969 / 15.09
The unadjusted wage gap is used to determine the average difference between earnings of men and women while the adjusted wage gap is used to control for variations in different, relevant factors such as career fields, experience, college major, and many other factors that can skew the results of the unadjusted gap. Because the adjusted value is less than the unadjusted value the unadjusted coefficients are used to calculate the explained and unexplained portions of this gap. With these calculations, based on qualifications alone, women should earn more than men in this field and the unexplained difference accounts for (17.58/15.09) 116.5% of the gender differences in wage.
With the national unadjusted pay gap being calculated from ranges of 66-82% the above calculated wage gap fits closer to the higher predicted measure of 66%, and is also lower than the unadjusted wage gap specifically for Ohio which has been estimated at 78 percent ("Ohio Women and the Wage Gap", 2016).
Policy Recommendations
Based on the results presented, not only in this paper, but also in the research reviewed, the policy recommendations include encouraging young women in fields of math and science; providing more flexibility within the labor market that would allow for women to continue their careers despite interruptions later in life; create programs that would allow women to maintain their career status during their time away, and prevent demotion due to this temporary leave. Another recommendation is to require equal employees equal pay, and to prevent negotiation from being an encouraging factor in an employee’s wage.
Conclusion
This paper explored the gender wage gap for Financial Analysts in 2015, using both the unadjusted and adjusted wages to conclude that there is, in fact a significant wage gap for this career field with both results concluding that women make less than 70 percent of the earnings of their male counterparts and that, focusing solely on qualifications, women should actually earn more than male financial analysts and that almost 117% of the gender wage gap is accounted for in the unexplained portion of the model. Many explanations have been presented though various research as to why this gap exists, and for this field the prominent explanation remains the lack of female engagement in the fields of math and science. Although, many alternative explanations and factors may contribute to this difference; the slight educational advantage of male students over female students, the environmental preferences of female workers, and both the up-front time commitment of the advanced degree and labor market time requirements to gain experience. Even with these discouragements and barriers for girls and women, there remains opportunity for change and the ability for women to succeed as equally as men do in these corporate and financial sectors as well as many other career fields.
Sources
"13-2051.00 – Financial Analysts." O*NET OnLine. N.p., 2016. Web. 31 Mar. 2017. <
Bertrand, Marianne, Claudia Goldin, and Lawrence F. Katz. "Dynamics of the Gender Gap for Young Professionals in the Financial and Corporate Sectors." American Economic Journal: Applied Economics 228.255 (2010): n. pag. American Economic Association. Web. <
Ellison, Glenn, and Ashley Swanson. "The Gender Gap in Secondary School Mathematics at High Achievement Levels: Evidence from the American Mathematics Competitions." Journal of Economic Perspectives 24.2 (10): n. pag. American Economic Association. Web. <
"Ohio Women and the Wage Gap." The National Partnership for Women & Families, Apr. 2016. Web. 1D4290035416DE50B36235D34D16C4E&rd=1&h=67ar0jNu5avsmaBXYmBqaZR3s7t rgIp03ora8DJWGp4&v=1&r=http%3a%2f%2f library%2fworkplace-fairness%2ffair-pay%2f4-2017-oh-wage- gap.pdf&p=DevEx,5060.1>.