genderragegap2.docx

MALE-FEMALE WAGE DIFFERENTIAL: A CASE STUDY FOR THE UNITED STATES
1 Introduction
The percentage ratio of U.S women full-time earning to men is 81.5% in 2019 and 81.1% in 2018, which gives an increase of 0.4 percent. This leaves a wage gap of 18.5% as compared with 2018 with 18.9%(Institute of Woman Policy Research).Wage disparity between males and females has long been a persistent issue across countries of the world. For decades, lower wages have been associated with female workers despite their educational and professional achievements. This topic has been a major focus of a large volume of research (Xin, 1997). The gender wage gap exists in the labor market, where male workers are usually paid comparatively higher wages than their female counterparts. This is a universal phenomenon arising from the perception of employers that females are less productive (Rahmah, 2011).
Although the wage gap has significantly reduced in recent times. The movements in gender wage differentials do not only reflect the personal characteristics of men and women in the labor force, but also complex shifts at the macro-level (Jing, 2014). Despite great improvement in women’s education and increasing participation in the labor market, the gender wage gap has remained substantial in developed countries like the United State.(Blau Kahn,2017).
The traditional ways of explaining the gender wage gap relied on gender differential in individual qualification and their reward; segregation of jobs by gender (Gustafson Li, 2000; Zhang, 2004). Meanwhile, studies have identified other factors such as individual characteristics and human capital endowments,(Lim, 2002; Madrigal, 2004; Urdinola Wodon, 2006), occupational segregation (Deutsch et al; 2004; Tenjo, 2006), interrelated factors including scope to reconciling work and family life (Bergmann, 1989). (Perticara Astudillo, 2010) identified four factors contributing to gender wage differentials. The demographic factors including age and ethnicity; human capital variables including education, training, and experience; job characteristics such as fulltime, part-time, permanent, contract and sectors.
The gender wage gap in the U.S was recorded to be 20%, as of 2016. The wage gap between men and women has been a subject of research for years in the U.S. which findings revealing that in labor markets women earn substantially less than men..After adjusting for difference in experience, industry type, education attainment, collective bargaining coverage and occupation, the gender pay differential is still substantial, with woman earning 8.4% less than man(Blau and Kahn 2017). Several studies investigate gender differential, seeking to infer the extent to which the gender wage gap is the consequence of disparate treatment by employers. The statistical exercise is one of comparison; the difference between the wages women receive and those earned by individuals who are male but otherwise comparable in terms of relevant characteristics (for example, men with similar levels of human capital) (Dan et al; 2015).
Since the second half of the 20th century, women’s labor force participation in the U.S. has grown significantly. Women are working longer hours and pursuing higher education in greater numbers (U.S. Bureau of Labor Statistics, 2019). However, despite this progress, significant wage gaps between men and women persist in the U.S, research has shown that women are not estimated to reach pay parity with men until 2059 (Center for American Progress, 2019), where the most recent Census Bureau data from 2018 revealed that women of all races earned, on average, just 82 cents for every $1 earned by men of all races (Robbin, 2020).
In a nutshell, findings made it clear that in the United States, more than 55 million working women earned an estimated $545.7 billion less than their male counterparts in 2019(U.S. Bureau of Labor Statistics, 2019). Thus, household responsibilities may reduce the amount of physical energy available for market work, and therefore may reduce worker productivity. Despite women’s large share in the labor force, wage gap continue to exist. In particular male-female pay differential affect the position of women in the labor market as well as the status and power of women within the household (Virginija, 2006).
This paper seeks to contribute to the growing literature addressing the issues of gender wage differentials across sectors of the economy in the United States, using the most recent data which was taken from the Current Population Survey (CPS) from January to December 2020. It also examines the current state of the gender wage gap in the U.S and how the well-being of female could be improved upon by providing answers to the following research questions: What are the relevance and determinants of human capital model and other demographic characteristics to gender wage differentials in the United States?
2 literature Review
Several studies have conducted research on the causes of wage differentials between men and women. Some of the studies postulated that individual characteristics (such as education, experience, occupation, and, sometimes, motivation, expectations, and field of study) and/or horizontal and vertical segregation in employment contribute to individual wage, but there still exists an unexplained wage gap. That is inequality in pay for similar work. Other studies stated that wage disparity is caused by occupational crowding and societal gender roles. This means that the population of women is high in a low-paying industry compared to men.
In a result of an analysis by (Blau and Kahn,1997), using data from the Panel Study of Income Dynamics (PSID) to examine the wage difference between male and female. They found the wage differential between the male and female fulltime worker to be 72.4 percent. After they adjusted for race, the ration increased to 80.07 percent. Adjusting for other variables (occupation, type of employment, experience, education attainment and unionism), this raised the ratio to 88.2 percent. A significant part of the wage differential remains unexplained, and this is as a result of discrimination in both specifications. Controlling for some variables such as occupation, unionism and type of industry may be questionable to some extend cause discrimination may also influence them to some extent.
(Wood, Corcoran and Courant, 1993) in their study of examining the wage difference between men and women, using the graduates of the University of Michigan Law School classes of 1972–75, 15 years after graduation. They found the pay gap between women and men to be minimal at the beginning of their careers, but women make 60 percent as much as men 15 years after graduation. Some of this difference is as a result of individual choice, such as hours of work done. After controlling for grades while in school, family status, experience, part-time worked, size of firm, type of employment and hours worked, a male still makes 13 percent more than the female. Also, in an analyze of wage differential among fresh college graduates in 1985 by (Weinberger,1998). After she adjusted for educational institutions they attended, there college major, and the average college grade point average specific educational institution attended. The unexplained portion of the wage differential between men and women was found to be 10 to 15 percent.
Using a data from the 1989 U.S. census, (Sparrowe and Iverson ,1999) studied the gender wage differential in the hospitality industry. After they adjusted for education attainment, workforce experience, number of hours worked, and occupation crowding. Sparrowe and Iverson still discovered female were substantially paid lesser than male with an income of $498 against a mean income of $4,673 across the entire hospital industry, despite controlling for these factors.
Cotton (1988) cited a flaw with Oaxaca(1973) work. Cotton stated that Oaxaca only did an analysis on whites and blacks, which is not realistic in the case of gender disparity. Cotton revised Oaxaca’s work by conducting analysis on the discriminatory wage factors based first on whites and then blacks and also differences in black and white’s productivity. The results show that around 49% of wage disparity is due to white male skill advantages. Kyyra (2007) observed that women, on average earn less than their male counterparts in the labor market. Greater preference is shown toward men as there’s an unequal distribution of both sexes across occupations, industries, firms, and jobs. This is also evident among white-collar workers; it stems from the disproportionate concentration of women within firms. Within firms, high-paid managerial jobs are mainly occupied by men, and among non-managerial jobs men are concentrated in positions with higher skill requirements. If the labor market segments are primarily occupied by women, they are likely to be paid lower than men on the average (Blau Kahn, 1997).
3 Theoretical Model and Hypothesis
In this section, I review the competitive and non-competitive theories of wage determination that attempt to explain the existence of wage differentials. A basic difference between both groups of theories rests on the hypothesis that wages do, or fail to adjust in to clear the labour market.
3.1 Human Capital Model
The human capital model links expected lifetime labor force participation to one’s incentive to acquire marketable training. According to (Polachek, 1981), women’s investment in human capital is on the low side because they expect to work outside the home for less of their adult lives and choose occupations to minimize losses associated with their more intermittent attachment to the labor force. In recent times, women have surpassed men in college enrolment, and are at or near parity in representation in most professional schools (Morris Western, 1999). Also, they have benefitted in the computer age than men and the real wage increases were concentrated among college educated (Leicht, 2008). However, given the traditional division of labor by gender in the family, women tend to accumulate less labor market experience than men and have lower incentives to invest in the labor market-oriented formal education and on the job training, and resulting in smaller human capital investments which lowers their earnings relative to those of men. The human capital model implies an important role for wage structure in explaining the gender wage gap.
Labor economists have argued that some groups are paid less because they have relatively low productivity. Thus, women’s lower wages are seen as a measure of their lower productivity (Becker, 1993; Darity Mason, 1998). Women are more likely than men to enroll in higher education; graduate from colleges rather than universities (Card Krueger, 1994). Women are also more likely than men to relocate for their spouses’ jobs. Thus, they avoid jobs requiring large investment in skills that are unique to a particular enterprise (Zhang, 2004). They tend to choose majors that lead to lower paying and choose to limit their investments in work experience due to their family obligations (Becker, 1993). Women’s choices are shaped by social values, conventions, and systems, gendered and social capital.
3.2 Occupational segregation
Occupations that have high proportions of women working in them are often referred to as feminized. It has been found that the higher the proportion of women who work in an occupation the lower the average pay within it (Blau Kahn, 2008; Bettio Verashchagina, 2009; Levanon et al., 2009). Both men and women within feminised occupations experience lower pay, although because women obviously outnumber men they are disproportionately affected.
3.3 Undervaluation Theory
The persistence of the gender pay gap suggests the possibility of a stigma associated with occupational feminization, that work done by women is socially and economically undervalued. This theory is most prevalent in the United States
(see, for example: England, 1992, 2005, 2010) though it is also supported in the UK ( Perales, 2013). The theory posits that society undervalues certain types of work precisely because women do it.
3.4 Gender Role Theory
Men and women often follow different paths in education and employment, which lead to overall differences in pay.These influence much of what happens in the home, school, personal relationships, family life, and employment (Lips, 2012; Ochsenfeld, 2014; Rubery, 2008). Rather, these choices are constrained by social pressures and expectations and are passed on from one generation to the next.
4 Method and Model Specification
This study is modeled using the (Oaxaca, 1973) wage decomposition. Oaxaca Blinder study mean outcome differences between groups (Ben, 2008). In his work, (Oaxaca, 1973) further explained decomposing the mean differences in log wages based on regression models on a counter-factual manner. Discrimination against females can be said to exist whenever the relative wage of males exceeds the relative wage that female would have obtained if males and females were paid according to the same criteria,only considering the individual characteristics that affect job performance.
He proposed that to calculate the wage decomposition, the equations of income for men and women is first estimated.The income equation estimated by Oaxaca (1973) for each group (by race and sex) has a semi-logarithmic functional form as follows:
(1) (Wm) = αm + Xmβm + um

(2) (Wf) = αf + Xfβf + uf

Where Wm is the log of wage, Xm and Xf is the vectors of mean values of the regressors for male and female respectively, βm and βf is the corresponding vectors of estimated coefficients U is the error term.
A simply way of calculating the gender wage gap is by subtracting the female income equation from the male income equation,and assumed the difference between the intercepts of the equation corresponds is equal to discrimination. However, Oaxaca suggest that the unexplained part of the difference is as a result of both: the differences in coefficients, as the differences in average characteristics of the female.
(3) Wm−Wf = βm(Xm−Xf)
(4) Wm−Wf = (αm−αf) + Xf(βm−βf)
The third equation represents the part of the wage gap that can be explained by differences in the observed characteristics of individuals(possessed by endowment) and equation four shows the unexplained portion of the differential and therefore its interpreted as the difference as a result of discrimination.
5 Data and Descriptive Analysis
The study uses cross-section samples taken from the monthly US Current Population Survey (CPS), for January 2020 to December 2020. The full number of observations for the sample was 302,332. The effective sample for whom sufficient information to obtain their usual hourly earnings existed amounted to 74,147 individuals. It should also be noted that the Stata software automatically removes observations for which there are missing values. The sample comprised of 51.1% male and 48.8%females. The statistical analysis used in this study includes males and females wage of workers between 23 and 79 years of age.The wage is measured as average earnings per week.The natural logarithm of the weekly wages is used as the dependent variable. Some of the explanatory factors examined in the analysis are average values relating to the most recent year. The explanatory factors examined in the analysis, for males, and females, include the indicator variables in which the value of the variable is one, if the characteristics is true or present and zero otherwise, for the worker’s marital status, race, educational attainment measured in terms of the years of schooling, industry, and part-time employment status; the percentage of workers who are females in the worker’s occupation and the worker’s industry. The mean values and the standard deviations of each of the factors listed above (among males and females in the sample used in the analysis) are presented in Table 1.
Table 1 shows the descriptive statistics of the variables used in the model. As expected, the mean wage of male workers is higher than that of female workers. In 2020, the mean logarithm of the hourly wages is 7.225 for women and 7.404 for men as shown in Table 1.The log gender wage gap between women and men is -0.18.Women is shown to have 0.323 more year in education attainment than men.The portion of women working par-time is 44.4% and 0.25% for men. This implies that women work less hours than men with the dffernce of -0.19.

1
1
1

(1)

Male

Female

mean

stand. dev

mean stand. dev mean

stand. dev

Lnwage

7.404475

.4523477

7.224718

.4314448

Education

12.25047

2.137501

12.57382

2.082274

Experience

21.47624

15.19673

22.44435

15.62042

White

.8148353

.3884362

.780477

.4139292

Black

.1075337

.3097946

.13607

.3428676

Spouse present

.4529441

.4977874

.4170716

.4930817

Spouse absent

.0154524

.123345

.0152385

.1225019

Widow

.0254727

.1575578

.0514576

.2209323

Divorced

.0937426

.2914741

.1329781

.3395558

Never married

.4076945

.4914123

.3623288

.4806798

Part-time

.2533555

.4349385

.4443739

.496903

Private

.0505498

.2190794

.061451

.240159

Self employed

.0296126

.1695184

.0763582

.2655741

Less than250000

.0874403

.282483

.0868761

.2816573

Less than500000

.0823774

.2749425

.0819346

.2742689

Less than1million

.134483

.3411751

.1325364

.3390777

Agriculture

.018142

.1334667

.0064598

.080114

Mining

.018142

.1334667

.0015735

.0396372

Transportation

.0285842

.1666371

.0094136

.0965675

Retail trade

.1531524

.3601391

.1686451

.3744433

Hospital

.0185112

.1347925

.0737356

.2613438

Teaching

.0292962

.1686377

.0673587

.2506456

Manager

.0329615

.1785382

.036716

.1880662

Legal related

.0021359

.0461671

.0064046

.0797731

Production .1644121 .370654 .0703125 .2556765

Table 1: Descriptive Statistic

6 Empirical Result
6.1 Individual characteristics wage differential regression for male and female
The actual gender wage gap reflects many aspects. Changes in the wage gap vary greatly across races, industries, occupations, regions, and cities. Table 2 shows the results obtained from the regression analysis. The data were divided into two, separated by gender to analyze the trend of the gender gap in the labor market.

As shown in table 2, the model indicates that about 29% and 27% for males and female of the variation in wages is explained by factors controlled for in the model, and the remaining 71% and 73% is explained by other factors for male and female, respectively.
The human capital variables; education and experience have a positive and significant effect on wages. Based on the coefficient of potential experience as presented in Table 2, an increase in one year of experience should yield a positive change in hourly wages by 0.5%, for both genders while the returns on education are between 3.9% and 3.8% which is slightly higher for males compared to females.
This is in line with the findings of (Blau Kahn, 2017) on years of education and work experience.. Further, this study found that job characteristics explain 60% of gender wage differentials. There is no notable difference in the wage structure between females and males is the years of experience in 2020, the rate of return of experience is only 0.0046 for women, while the rate of return of experience is 0.0047 for men. Traditionally, experience has been an important factor in the determination of wages for men. This is in line with the findings of (Jingyo, 2020), the rate of return for whites indicates that the wage difference between white continues to be higher for men than women in the year under
review.
Most of the variables were found to be statistically significant at the 1% level, except for agricultural-related jobs. The returns for the agriculture sector were insignificant for both gender while the returns on non-agricultural sectors were positive and significant for both males and females. This is against the findings of (Deininger et al; 2013) they revealed that discrimination is more pronounced in the primary(agricultural) sector than in the non-agriculture sector.
6.2 Decomposition of the Gender Wage Differential
The effect of discrimination is the residual left after subtracting the effects of differences in individual characteristics from the overall wage differential. The calculations based on individual characteristics for both gender is presented in Table 3.
The human capital and job characteristics factors explained only 5% of the gender wage gap.Discrimination (unexplained portion) contributed the major portion (about 94%) of the trend the wage gap as seen in table 3.
Table3: Oaxaca Wage Decomposition Estimate

7 summary

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