Gender

The gender and race composition of median household income and male/female, white/black pay gap. Wyoming, USA


I.                  
Introduction

Gender wage differentials seem to be a constant in modern world. There has been continued progress in gender equality in paid employment in over past 3 decades, due largely to women’s progress in education and workforce participation, but still it we are unable to come up with equal wages without being gender biased. Gender pay gap has lifelong financial effects. For one, it contributes directly to women’s poverty.

In the traditional economic theory such as monopsony theory (Jaon Robinson), this differential is the result of women’s lower measured and unmeasured labor skills and or a result of labor market discrimination (New joint Negotiating Committee of Higher Education Staff). Although women are now working in more fields than ever, they are still more likely to work in lower-paying jobs than men are, and they remain under-represented in many occupations.

This paper try to investigate whether the gap still exists between men and women. Hence the research question is: Is there and disparities by gender and race in the wages of full time worker?

For my research, I have taken Wyoming as my case example. The reason to select Wyoming as a case is; asper AAUW 2015 Spring report Wyoming is the city with largest wage gap difference between men and women.  In this paper, I will employ 2015American Fact Finder ACS data to investigate the wage difference between the gender.

The paper is organized as follows: In first section I present different theories related to my research and hypothesis for testing my research question. In second part I talk about the data and methodology for testing my hypothesis. Third section will present the results and findings and the last section will give some concluding remarks.

II.                Theory and Hypothesis

Sex and racial segregation at work are intimately liked to sex and racial inequalities in the labor force. Sex and racial segregation are profoundly liked to the quality of jobs. This is because the best jobs in most workplaces are systematically reserved for white males. In addition, as a job comes to be thought of as “women’s work” or a “minority position”, it tends to be downgraded in prestige in an organization. (Tomaskovic-Devey, Gender & Racial Inequality at Work: The Sources and Consequences of Job segregation, 1993)

Human Capital Model by Mincer and Polachek (1974) explains gender pay difference in earning by difference in productivity. As per this theory women except shorter and more volatile work lives than man, and this implies less incentives to pursue strong human capital investments, which causes lower economic outcomes. But this theory is argued on many aspects, mainly because of its emphasis on labor-supply-side considerations.

Bergmann (1974) develops another theory which argues the discriminatory exclusion of women from male jobs results in an excess supply of labor in ‘female’ occupations, depressing wages for otherwise equally skilled and productive workers. Aigner and Cain (1977) and Lundberg and Startz(1983) developed a theory based on labor –demand-side considerations. This theory emphasizes the role of imperfect labor markets, where the problem of symmetric information causes a drop in the mean wages of women, given that employers do not know if they are hiring a low productivity or a high productivity female worker. In this model, firms can more easily predict the performance of whites or men than of minorities or woman. In such a world, firms offer different wages schedules to, say, white and blacks.

Clau and Kahn(1995) as men and women tend to have different levels of labor markets skills and to work in different sectors, there is a potential important role for wages structure in determining the gender pay gap.

There is less known about race segregation, but research has found that as the percent minority in an occupation rise, earnings tend to decline for minorities and majorities alike. The status composition is that jobs that are dis proportionately female or black become stereotyped, and the work process itself begins to reflect the devalued master status of typical incumbents (Acker & Van Houten 1974; Bielby & Baron 1985, Caplow 1954, Treiman & Hartmann 1982). According to this argument, jobs and organizational structure may be fundamentally determined by race and gender.

All these theories, however share feature that gender specific factors are considered the main source of the gender wage differential.

Hypothesis

We will assume following hypothesis to test the research questions

Null Hypothesis: There is no difference between the median household income of male and median household income of female of all the racial groups.

H0: µwhite/m/in = µwhite/f/in = µblack/m/in = µblack/f/in = µWhitenonHispanic/m/in = µWhitenonHispanic/f/in = µ other/m/in = µother/f/in  

Alternative Hypothesis: There is a significant difference between the median household income of male and median household income of female of at least one racial group.

H1: µwhite/m/in ≠ µwhite/f/in µblack/m/in ≠ µblack/f/in ≠ µWhitenonHispanic/m/in ≠ µWhitenonHispanic/f/in µ other/m/in ≠  µother/f/in 

III.             Method and Data

Method

In the process of examining the relationship between variables, researchers can use Two way ANOVA to compare the means of two or more groups.

Data Set

The result in this article is based in census tract data of Wyoming collected by American Fact Finder ACS data of 2015. The data contain demographic characteristics, median earning of full time year-round workers and ethnic origin of all the census tract in Wyoming. I used 3 ethnic group, White, Black, White, and others for my study.

Research can examine the relationships between two variables by comparing the mean of the dependent variable between two or more groups within independent variables. The sample is divided into two groups based gender and ethnicity on independent variable. Then compared the means of the median income of full time on the dependent variables.

Table 1: Description of Variables

Variables Definition Level of Measurement Source
Independent Variable Gender Gender is a social construction whereby a society or culture assigns certain tendencies or behaviors the labels of masculine or feminine.

It can be divided in two categories, Male or Female

Nominal https://www.census.gov/glossary/#term_Gender
Racial Groups include race and national origin or sociocultural groups.

White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or other Pacific Islander. Some Other Race. Respondents may report more than one race.

Nominal https://www.census.gov/glossary/#term_Race
Dependent Variable Median House hold Income the median household income is based on the distribution of the total number of households and families including those with no income. The median household income for individuals is based on individuals 15 years old and over with income. Median income for households, families, and individuals is computed based on a standard distribution. Interval-Ration https://www.census.gov/glossary/#term_medianincome

For racial groups, I have 4 categories,

  1. White: A person having origins in any of the original peoples of Europe, the Middle East, or North Africa. It includes people who indicate their race as “White” or report entries such as Irish, German, Italian, Lebanese, Arab, Moroccan, or Caucasian.
  2. Black/African American: A person having origins in any of the Black racial groups of Africa. It includes people who indicate their race as “Black, African Am., or Negro”; or report entries such as African American, Kenyan, Nigerian, or Haitian.
  3. White Non- Hispanic: whitesnot of Hispanic or Latino origin or “Anglo,” are people in the United States who, as defined by the Census Bureau, are considered racially white and are not of Hispanic or Latino origin/ethnicity.
  4. Others: The remaining categories in race are combined and consolidated in this category.

Descriptive statistics was conducted to observe the distribution of the data. Table 2 presents a summary of the entire sample aged above 15. These data are used throughout the paper.

 

 

Table 2: Descriptive Statistics of census tract, Wyoming

Male Median Household Income
  Minimum Maximum Mean Median Mode Standard Deviation Range Count
White Worker 25875 82260 52515.92 52154 50147 10361.43 56385 100
White non-Hispanic workers 27791 82500 54124.1 53164 62119 10997.9 54709 101
Black Workers 32675 35903 34289 34289 n/a 2282.54 2
Other Race Workers 0 250000 48081.5 34108 n/a 59452.77 250000 14
Female Median Income
 
White Worker 13519 51532 35263.60 33570 32500 6446.31 38013 100
White non-Hispanic workers 13519 53889 35665 34011 33654 6600.38 40370 101
Black Workers 26000 31750 28548.33 27895 n/a 2930.14 5750 3
Other Race Workers 0 60417 25689.77 30000 n/a 17232.27 60417 13

(American Fact Finder ACS 2015 data, 2015)

There is a difference in mean of white male and black male wages as well as the mean on all the groups in female and male have large differences.

IV.             Results and Findings

The two-way ANOVA was conducted to evaluate the relationship between male and female wage structure within different ethnic groups.  Thought the AAUW report of 2017 state that there is 64% (AAUW, 2017) of gender pay difference between male and female wages, but there is no substantial difference between the wages of racial groups. Table 3 displays the summary for ANOVA.

Table 3: ANOVA test

SUMMARY Count Sum Average Variance    
White Worker 2 87779.52

 

43889.76

 

148821272.7

 

White non-Hispanic workers 2 89789.05

 

44894.52

 

170370081.9

 

Black Workers 2 64778

 

32389

 

24696392

 

Other Race Workers 2 64108

 

32504

 

8427832

 

 
Gender
  Male 4 176651 44162.80 112775130.3
  Female 4 129804 32450.90 12345124.5
 
ANOVA
Source of Variation SS df MS                F P-value F critical
Rows 297370848.9 3 99123616.3 3.812 0.15 9.276
Columns 274335663.28 1 274335663.28 10.552 0.047 10.128
Error 77989915.35 3 25996638.45
 
Total 649696427.54 7

 

The ANOVA was insignificant for male and female wage difference as Fobtained < Fcritical, and P-value > α-value. The result of ANOVA allowed to fail to reject the null hypothesis and supporting the conclusion that there is no statistical disparity between the wages of male and female between different racial groups.

As the study state that we have substantially overcome the wage difference from last century.

V.                Summary and Conclusion

The purpose of this study was to examine the disparity between the media wages of male and female full time workers within different ethnic groups. Descriptive statistics allowed to determining the mean of the independent variable and dependent variable. The gender pay gap may decreased in last 2 decades and the study shows there no significant difference in wages of male and female within racial settings.

 

 

 

References

AAUW. (2017). The Simple Truth about the Gender Pay Gap.

American Fact Finder ACS 2015 data. (2015).

New joint Negotiating Committee of Higher Education Staff. (n.d.). The Gender Pay gap-Literature Review.

Sow, M. T. (2014). Using ANOVA to Examine the Relationship between Safety and Security And Human Development. Journal of International Business and Economics, 101-106.

Tomaskovic-Devey, D. (1993). Gender & Racial Inequality at Work: The Sources and Consequences of Job segregation. Ithaca, New York: ILR Press.

Tomaskovic-Devey, D. (1993). The Gender and Race Composition of Jobs and the Male/Female, White/Black Pay Gaps. Social Forces, 45-76.

AAUW. (2017). The Simple Truth about the Gender Pay Gap.

American Fact Finder ACS 2015 data. (2015).

New joint Negotiating Committee of Higher Education Staff. (n.d.). The Gender Pay gap-Literature Review

 

List of Tables

Table 1: Variables. 4

Table 2: Descriptive Statistics of census tract, Wyoming. 6

Table 3: ANOVA test 7