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Gender-Based Contrast of Work Income and Its Drivers Research Paper


Introduction

It is often reasoned by the public that women get less remuneration compared to men due to gender distinction while some believe that women receive less salary for the reason that they are less competent at work. Interestingly, some recent reports have revealed that women are not less capable than men rather they perform better than male workers in different areas of work. This recent discovery leads the subject of wage gaps towards gender biases in the workplace which should be addressed to have a vibrant idea on the matter.

The fundamental objective of the paper is to identify the drives of wage rate discrepancies between men and women as well as the proposed study will help us to conclude if the lower pay to women is vindicated or not. It was found in many research papers that the difference in work-related skills and a lack of confidence are dominant variables for creating a gap in the wage rates which seems to be unagreeable. It is assumed, the following questions would help the study to reveal the reasons for heterogeneity in the workplace- Is there any difference in skills between men and women due to the distinction in educational qualification? And How the participation rate in the workforce is related to the wage rate? The answers to these questions will help us to identify the reasons for wage discrimination in the country which will open door to further research opportunities in the field and to improve the working conditions in the USA.

The study believes that even after ensuring an improved higher education rate and growing participation in the workforce women have failed to secure an equal wage rate due to acute gender biases in the workplace. In other words, the study will quantify the impact of gender biases on the wage rates of women in the context of labor force participation and the level of education in the USA.

Literature Review

The wage rate difference between men and women is not a recent phenomenon rather it has been an issue for years- that women are paid less than men not only at the workplace but also in society. Card, Heining, and Kline (2013) measured wage rates gaps in terms of education, occupation, and industry dissimilarities. Their study discovered that the gaps are increasing due to the robust influence of job nature, the relation of workers with the companies, and project establishments. They also mentioned in their report that previous studies had already established that working skills are major criteria for deciding the wage of a worker. Supporting their finding Koellinger, Minniti, and Schade (2013) proved that women are less confident, possess a different set of work-related skills, engage with coworkers differently, and exhibit more fear at the workplace. These findings justify why employers have developed an idea that women are less efficient than men at the workplace and they deserve less wage than men.

Interestingly Delfgaauw, Dur, Sol, and Verbeke (2013) didn’t find any difference in sales performance while they experimented on different sales teams of men and women. Similarly, Roth, Purvis, and Bobko (2012) measured the performance of different gender in the fieldwork and their study result didn’t find any significant difference in the work performance, but the study revealed that men show better managerial skills than women. Findings of Delfgaauw et al. (2013) and Roth et al. (2012) suggesting that the work performance of women is almost equal to the performance of men excluding the instance of management position.

Considering the fact, offering higher wages to men for the managerial position would seem justified, but offering higher wages to men for laborious work doesn’t make a proper sense as there is no difference between the work performance of men and women. The only answer to this particular issue that will make sense is- the workforce is still failing to overcome gender stereotypes, women are being paid less wage not because they work differently than men, as it doesn’t reduce their work performance, but because they are women. According to Heilman (2012) women are not yet free of the burden of gender stereotypes, they are still considered incompetent in the male gender-typed position, their behaviors are still inadequate in the work context, and if they succeed in achieving the goals they are disliked. Therefore, the sole reason for wage difference seems to be the gender stereotyping at the workplace.

Methodology

The study will be conducted based on secondary data sources which mean there will be no need of primary survey for collecting the required data. Required information on wage rates and other identified variables such as labor force participation, education, the percentage of women participation in the workforce will be collected for further analysis. In addition to it, different credible online sources, and websites will be used for collecting the required information. Several descriptive statistical tools will be used to analyze the obtained secondary information. Finally, the findings of both qualitative and quantitative research shall be considered to conclude the research hypothesis.

For the analysis of data, a linear regression equation will be used where independent variable y is the wage rate of women and the dependent variables are men wage (x1), women labor for participation (x2), and men labor force participation (x3). In other words, the equation will regress the impact of men wage and labor force participation on the wage rate of people in the USA. A test will also be conducted on the income effect of different levels of education to provide evidence of the influence of higher education on wage rates.

Data Collection

This research is heavily dependent on secondary data which is collected from the FRED economic data. The average weekly wage data of men and women is collected from the source to analyze the extent of wage discrimination. The data consists of 37 years (from 1979 to 2016) past wages that were paid to men and women per week. Additionally, labor force participation data of both the gender was collected for 37 years (1979 to 2016) from the same source. The data includes salary and wage information of employees in both private and public sectors who were working for earning salary or wages. And the data do not include professionals such as self-employed people, businessmen, and others who do not earn a salary but earn a profit. The data is before tax and all other deductible income, which includes but is not limited to commission, tips, overtime, and proxy, etc.

Table 1. Average weekly wages.

Women Earnings Men Earnings
Mean 489.58 647.89
Standard Error 28.91 31.45
Median 473.50 627.00
Standard Deviation 178.19 193.90
Sample Variance 31752.14 37597.18
Kurtosis -1.26 -1.25
Skewness 0.07 0.05
Range 589.00 655.00
Minimum 195.00 314.00
Maximum 784.00 969.00

To find out the trend of the gender gap in salary and wages data of men and women weekly average earnings in the USA was collected from FRED Economic data: Wage and salary workers: 25 years and over: Women (2017) and FRED Economic data: Wage and salary workers: 25 years and over: Men (2017). Labor force participation data was also selected to establish a relation between labor for participation trend and wage, the data was collected from FRED Economic data: Gender based labor force participation: Women (2017) and FRED Economic Data: Gender based labor force participation: Men (2017). A set of data on education level and the weekly salary was downloaded from the same source (FRED Economic data: Wage and salary workers: Bachelor’s degree and higher, 2017& FRED Economic data: Wage and salary workers: High School graduates, no college), and finally, some reported statistics from Pew research center (2013) was used to support the hypothesis of the study.

Women median weekly earnings compared to men's weekly earnings.
Figure 1. Women median weekly earnings compared to men’s weekly earnings.

Results

The regression equation is suggesting the following result for determining the wage,

Results

which is suggesting that salary of women goes up by $.8904 with the improvement in $1 of men wages while 1% increase of women in the labor force reduces their wage by.13% times. On the contrary, 1% increase in men participating in the labor force decreases the women’s salary by 2.45% which explains that men workers are more preferred than women workers.

Table 2. Regression Statistics.

Multiple R 0.999076301
R Square 0.998153455
Adjusted R Square 0.997990524
Standard Error 7.987813123
Observations 38
ANOVA
df SS MS F Significance F
Regression 3 1172659.888 390886.6293 6126.254343 1.60257E-46
Residual 34 2169.375389 63.80515849
Total 37 1174829.263
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 102.45 172.026 0.595 0.555 -247.160 452.037 -247.160 452.037
Men Earnings 0.890 0.036 24.439 0.000 0.816 0.964 0.816 0.964
Women Labor force participation -0.132 0.958 -0.138 0.891 -2.078 1.814 -2.078 1.814
Men Labor Force Participation -2.447 2.490 -0.983 0.333 -7.507 2.613 -7.507 2.613

The correlation coefficient is suggesting that all the dependent variables are perfectly correlated (.999) with the independent variables and adjusted R2 s suggesting almost all the variations (99.79%) is explained by the data. Although there could be a standard error of 7.99 in the result. The P value is suggesting that the probability that women labor force participation and men labor force participation have a significant impact on the wage rate changes of women. Unlike, men’s earnings are not correlated with the earnings of women as suggested by the P value which is 0%. The statistics are providing a good base for the analysis which is suggesting men’s earnings are of high t value 24.44 which is unusual while men labor force participation has a t statistic of -.983 and women labor force participation has -.138.

The result of the regression does not show any significant difference for the changes in confidence level, for 90% and 99% confidence level the t statistics and the coefficients remain the same with a little or no change.

Women labor force participation Line Fit Plot.
Figure 2. Women labor force participation Line Fit Plot.
Men labor force participation Line Fit Plot.
Figure 3. Men labor force participation Line Fit Plot.

Data is suggesting that in the last 35 years women have been paid a lower wage than men, although the chart is suggesting that the gap is reduced by 20% in the last 35 years which is a positive indication. The difference in skills between men and women is considered as one of the most influential factors that are responsible for the difference in the wage rate. And the level of education can be assumed as an indicator of skills to a certain extent, so measuring the relationship between the level of education and weekly wage rate would have proven to be beneficial for the study. The data is suggesting that there is a positive relationship between education and wage rate, it is found from last 15 years data that a high school graduate without a college degree had earned an average weekly wage of $602 whereas a bachelor or higher degree holder made an average of $1072 per week which is 77.96% higher.

Table 3.

t-Test: Paired Two Sample for Means
High School Graduates or higher
Mean 601.875 1072.1875
Variance 2779.583333 11788.82917
Observations 16 16
Pearson Correlation 0.989312548
Hypothesized Mean Difference 0
df 15
t Stat -33.03950932
P(T<=t) one-tail 9.97636E-16
t Critical one-tail 1.753050356
P(T<=t) two-tail 1.99527E-15
t Critical two-tail 2.131449546

The chart is showing the analysis of wage rate gaps between high school graduates and university graduates or higher. This can be seen from the analysis (table) that the hypothesis test rejects the null hypothesis, in other words, the salary of high school graduates and university graduates are not equal. The changes in the wages were positively correlated by.9893 which is suggesting an increase in wage and salary is not affected by the level of education.

Education and Wage gap.
Figure 4. Education and Wage gap.

A report published by Pew research center (2013) shows that although only 12% of women had a graduate degree in 1970, in 2013 a higher percentage (31%) of women started their careers with a bachelor’s degree which is 7% higher than men (38%). If education would help to determine the wage rate of women then the wage rate of women should have been healthier than men in recent years.

The findings of the analysis are suggesting that men’s earnings are not directly correlated to women’s earnings and therefore we reject the null hypothesis “women’s wage rate is depended on men’s salary

Results

On the contrary, the labor force participation data reveals that men workers are more preferred in the workforce and when male workers increase in the market the wage of female workers goes down by a significant rate (2.47%). Therefore, the study failed to reject the null hypothesis “women receives low salary due to gender distinction

Results

and

Results

Limitations

The report was prepared to find out how the relationship between education and labor force participation rate is influencing the wage rates gap in the USA. Although the report is completed successfully the usage of the result of the paper would be limited due to the following limitations of the study-

  1. The study had time constraints as it was completed within a short time frame
  2. The analysis was done based on secondary sources of data only
  3. A primary survey would have been more effective for supporting the hypotheses but it wasn’t done due to time constraints
  4. The study on large sample would provide better insights
  5. Limited access to required data was one of the major constraints for the study
  6. An analysis of other variables such as workplace performance, women’s contribution to firm value, average work time of women could reveal a better relationship between data.

Further researches can be done to find out the reasons for workplace discrimination and their impact on the wage rates of women. The impact of workplace performance differences on the wage rates of the female workers can also be analyzed. The psychological variables that have an impact on the wage policies of the country can also be studied. It is also suggested that primary research on working women on their view of the distinction could bring some exciting information. It is also suggested to future researchers that a detailed study on the wage gap created by the education gap and gender distinction can be conducted to generalize the findings of this study.

Conclusion

A notable number of socio-economic studies have concluded that differences in education, distinction in skills, and nature of the work are relatively influential wage rate-determining factors (Card, Heining, &Kline, 2013). It is also proved that women display various working skills that are entirely different from the working skills of men (Koellinger et al., 2013). Therefore, it would be rational to conclude that there should be a variance in work performance between men and women because of their dissimilarities in working skills. Surprisingly Delfgaauw et al. (2013) and Roth et al. (2012)concluded, although we can see differences in working skills between men and women it doesn’t have any significant impact on these gender groups’ job performance. If the gender difference does not have any impact on the job performance then there should be no gap between wages, but the data (Excel workbook) is showing that higher education bids better wage rates. Unfortunately, the impact of higher education on the wage rate is proved to be ineffective in the wage rates of women.

In addition to that, the input of female workers in the labor market is dubious compared to the impact of the contribution of male workers on the wage rates as the analysis failed to reject the null hypothesis of the study. Therefore, it can be concluded from the statistical and theoretical evidence that the wage rates of women should have either been higher or equal than men but it is rather lower than the wage of men. And the only variable that can justify this irrational wage rate is acute gender stereotyping in the workforce.

References

Delfgaauw, J., Dur, R., Sol, J., & Verbeke, W. (2013). Journal of Labor Economics, 31(2), 305-326. Web.

FRED Economic Data. (2017). Web.

FRED Economic Data. (2017). Web.

FRED Economic Data. (2017). Web.

FRED Economic Data. (2017). Web.

FRED Economic Data. (2017). Web.

FRED Economic Data. (2017). Web.

Heilman, M. E. (2012). Gender stereotypes and workplace bias. Research in organizational Behavior, 32, 113-135. Web.

Koellinger, P., Minniti, M., & Schade, C. (2013). Gender differences in entrepreneurial propensity. Oxford bulletin of economics and statistics, 75(2), 213-234. Web.

Pew Research Center. (2013). Web.

Roth, P. L., Purvis, K. L., & Bobko, P. (2012). A meta-analysis of gender group differences for measures of job performance in field studies. Journal of Management, 38(2), 719-739. Web.

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IvyPanda. (2021, January 13). Gender-Based Contrast of Work Income and Its Drivers. Retrieved from https://ivypanda.com/essays/gender-based-contrast-of-work-income-and-its-drivers/

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"Gender-Based Contrast of Work Income and Its Drivers." IvyPanda, 13 Jan. 2021, ivypanda.com/essays/gender-based-contrast-of-work-income-and-its-drivers/.

1. IvyPanda. "Gender-Based Contrast of Work Income and Its Drivers." January 13, 2021. https://ivypanda.com/essays/gender-based-contrast-of-work-income-and-its-drivers/.


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IvyPanda. "Gender-Based Contrast of Work Income and Its Drivers." January 13, 2021. https://ivypanda.com/essays/gender-based-contrast-of-work-income-and-its-drivers/.

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IvyPanda. 2021. "Gender-Based Contrast of Work Income and Its Drivers." January 13, 2021. https://ivypanda.com/essays/gender-based-contrast-of-work-income-and-its-drivers/.

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IvyPanda. (2021) 'Gender-Based Contrast of Work Income and Its Drivers'. 13 January.

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