The Gender Wage Differential: A Result of Discrimination ...



The Gender Wage Differential: A Result of Discrimination and Personal Traits

Submitted to

Dr. Eric Dodge

Econometrics

Hanover College

December 5, 2003

By

Nicole Nakamura

Hanover College

Contents

Abstract 3

Introduction 4

Literature Review 6

Data and Model Specifications 11

Variables 11

Model 13

Econometric Issues 14

Data and Empirical Results 16

Variable Definitions 16

Data Problems 18

Empirical Results 19

Conclusion 24

Appendices 25

Abstract

Men typically earn a higher labor wage than women. The causes of this wage differential have been the focus of study and scrutiny by economists, politicians, social scientists, and many others. The debate has been over whether the differential is due to differences in skill and personal characteristics or due to discrimination. If the wage differential is the result of differences in skill between men and women, the gap is explained; however, when the wage differential is the result of discrimination, the gap is unexplained and unjustified. This study tests whether the gender wage differential is a result of discrimination. Furthermore, this study reports on which factors are thought to be relevant when determining a person’s wage. The results show that there remains an unexplained gender wage differential of approximately 11%; that is, if a person is male, he is likely to receive wages 11% higher than if that person were female. Therefore, according to this study, an unexplained gender wage differential does exist and is attributed to discrimination in the labor market.

Introduction

The factors that contribute to a person’s labor wage intrigue and puzzle many economists, employers, and employees. Because most people earn a labor wage, determining the factors that maximize potential earnings is imperative. Moreover, determining why one person may make more money than another while doing the same task is crucial in understanding the labor market. A plethora of authors, both within the United States and internationally have studied the gender wage differential in hopes of uncovering the factors that set men’s wages above women’s wages. Historically, men tend to earn more money than women, even while doing equal work; though recently, the implementation of federal laws such as the Equal Pay Act of 1963 and increased women opportunities have helped to reduce the gender wage gap. However, even with added political and social action, many researchers conclude that a gender wage gap still remains. The gender wage gap can be split into two explanations: one, that males, typically, are more skillfully qualified (ex. education, experience) than women, making the gender wage gap justified. Second, that after adjusting and accounting for various personal characteristics and skills that contribute to a person’s wage, the gap is due to discrimination. The main focus of this paper is to test whether a gender wage differential exists between full-time, hourly waged individuals based upon discrimination.

In this paper, I report the results of the gender wage gap due to discrimination as well as the effects certain personal traits have on a person’s wage. All data is gathered from a monthly survey conducted by the Bureau of Labor Statistics’ Current Population Survey. Though complex, highly detailed wage decompositions are given in the literary readings, a more comprehendible decomposition found in Studenmund (2001) is used. The results indicate that, holding all personal traits constant, there remains a wage differential of approximately 11%, which is attributed to discrimination. Furthermore, findings show that acquiring certain skills and characteristics influence a person’s wage significantly.

Literature Review

The gender wage differential has long been the object of empirical study and scrutiny. Many argue that accounting for all explained personal traits that contribute to a person’s wage, there still remains an unexplained wage gap. In other words, there is unequal pay for equal work. Others argue that the gender wage gap is a result of occupational crowding and societal gender roles. That is, women are more heavily populated in low waged industries as compared to men, and this type of discrimination is what causes the wage gap. As stated by Fields et al. (1995), there are some important sub-categories that need to be kept in mind while conducting a gender wage differential study. The first is that the wage differential could be caused by inter-industry wages. Second, the wage differential could be caused by the gender distribution among different industries. Third, the wage differential could be caused by productivity related factors. These sub-categories, according to Fields et al., are the various causes of the gender wage differential.

The factors causing the gender wage differential, both explained and unexplained, are difficult to measure, leaving the model highly susceptible to error. Gunderson (1989) lists some common problems that typically arise in a gender wage differential study. In all cases, there will be omitted variables; capturing all relevant variables is often times exhaustive, unattainable, or un-measurable (ex: Each person has different personal characteristics that may have some determination of their specific wage). Omitted variables result in biased coefficients that may over or understate the variables effect on the differential. Another common problem is that the occupational variables are too broad; significant differentials may actually lie within the industry, therefore, causing the gender wage differential to be understated. Correlation between two or more independent variables is also a potential threat. Many variables that determine a person’s wage are also determinant of each other.

Oaxaca (1973) studies the gender wage differentials in the urban labor market. The unexplained gender wage gap, discrimination, is measured as the male-female wage ratio without discrimination ((WM/WF)0) subtracted from the observed male-female ratio (WM/WF) which is then divided by the male-female ratio without discrimination.

D = WM/WF – (WM/WF)0

(WM/WF)0

The male-female wage differential is classified as:

ln Wm-WF = ∑ biM (XiM-XiF) + ∑ XiF (biM-biF)

The first term on the right represents the differences in characteristics evaluated at the male return (explained). The last term on the right represents differences in return for the same work, discrimination (unexplained). Gunderson (1989), Montgomery et al. (2003), and O’Neill et al. (1993) all use this estimator of the gender wage gap.

Oaxaca includes these independent variables:

• Years of schooling completed

• Class of worker: government employed, and self-employed with non-union private wage and salary workers as the reference group

• Industry

• Occupation

• Health Problems

• Part-Time Worker

• Migrant Worker

• Marital Status

• Size of Urban Area

• Region: North East, North Central, and West with South as the reference group;

• Potential Experience

• Children

Oaxaca conducts regression analysis by dividing male from female and further dividing whites from blacks. He then runs two regressions, full-scale wage regressions (includes occupation and industry) and one including only personal characteristics. Split regressions are used, because only a broad range of occupations and industries are included; thus, wage differentials are underestimated in the full-scale model. The results for the full-scale regression model show that discrimination accounts for 58.4% of the wage differential for whites and 55.6% for blacks. The results for the personal characteristics regression show that discrimination accounts for 77.7% of the wage differential for whites and 93.6% for blacks. Oaxaca concludes that unequal pay for equal work is not a large part of the gender wage differential, but rather, large numbers of women tend to be employed in low-waged industries, thus causing the large wage differentials.

Cotton’s (1988) cites a flaw within the Oaxaca (1973) study. Oaxaca divides whites from blacks when conducting the gender wage differentials. However, Oaxaca attempts to measure the differential with “demand-side sources of discrimination” even when the sources originate on the supply-side (Cotton 237). For example, in the past, blacks were discriminated against in education and other “skill acquiring opportunities,” leaving them at an automatic skill disadvantage even without discrimination (Cotton 238). The same education and skill disadvantage can hold true for women, though as time goes on, the disadvantage is diminishing. Cotton constructs a revision to the Oaxaca wage differential equation:

ln WW – ln WB = ∑Bi* (XiW-XiB) + ∑XiW (BiW-Bi*) + ∑XiB (Bi*-BiB)

B* is the non-discriminatory wage structure. The first term on the right represents the “difference in the current white and black average productivity characteristics evaluated as the market would in the absence of discrimination. It is therefore the ‘true’ value of the skill component of the wage differential” (Cotton 238). The second and third terms are discriminatory wage factors based first on whites and then on blacks. The regression results show that around 49% of the wage differential is due to white males’ skill advantages or 71 cents of the $1.44 wage gap.

Fields et al. (1995) compares the inter-industry gender wage differentials. They use independent variables of years of education, potential experience (age-yrs of schooling-6), urban residence (central city vs. other), size of city, region (Northeast, South, West), marital status, race (non-white vs. white), occupation type, industry. The results show that 13-19% of the wage gap is due to women in low paying industries, and 0-22% of the overall wage gap is explained by differences in inter-industry wage coefficients. They also find that female hourly wages differ significantly across industries.

Horrace et al. (2001) cite two shortcomings of Fields et al. (1995). First, Fields et al. does not state any of the standard errors for the regressions. The standard errors are critical in determining whether the results are significant. After looking back at Fields et al., standard errors are not mentioned but the level of significance and type of tests done are noted . Second, the gender wage gap does not remain constant or unchanged to “omitted reference groups of the binary variables in the model…the intercept term catches omitted industry and also the omitted category for any other binary variables in the specification (ex. race, occupation, marriage)” (Horrace et al. 612). Horrace and Oaxaca construct an alternative equation:

Gender Wage Gap = (βiF – βiM) + (αF – αM) + xF (θF – θM)

The first term on the right refers to the unexplained difference in wage earnings. The second term represents the changes in the intercepts of male and female wage equations. The third term is the changes in the slope parameters. The last term is used to offset the changes in the intercept changes (second term). Because the gender wage gap “varies with the mean characteristics of the female workers in each industry,” a vector average (xF) is used rather than xiF for each individual industry.

Studenmund (2001) does not conduct a gender wage differential study but rather explains and justifies the methods in setting up such a model. In Studenmund’s example, an earnings equation is set up based on various factors that influence a person’s wage, including gender.

Wagei = B0 + B1Di + B2Xi + Ei

Di = A binomial variable representing whether a person was male or female

Xi = A vector of factors influencing a person’s wage

Observing Di will reveal if a discriminatory wage differential exists between men and women. When Di is being observed, all other independent variables are accounted for and held constant, leaving only the unexplained wage gap.

Data and Model Specifications

The main purpose of this study is to test whether a gender wage differential exists between full-time, hourly waged individuals based upon various personal traits as well as discrimination. Oaxaca (1973), Fields et al. (1995), and O’Neil et al. (1993) have all concluded that more women than men are crowded into the low skilled part-time work sector, which contributes greatly to the appearance of the large gender wage differential. This study focuses on the gender wage differential between full-time workers, therefore hoping to capture a more accurate wage differential. The dependent variable is the ith person’s hourly wage. The independent variables reflect those chosen by past studies.

Gender: Binomial variable ‘1’ given for male and ‘0’ for female.

Potential experience: Classified as age minus years of schooling minus 6.

Marital status: Binomial variable ‘1’ given for married and ‘0’ for single or divorced.

Number of hours worked (week): The number of hours the ith person works in a typical work week.

Worker Class: Binomial variable of ‘1’ given for government worker (state, local, federal) and ‘0’ for private worker (profit and non-profit).

Geographical region: Binomial variables given for Northeast, Midwest, West with South as the reference group.

Union member: Binomial variable of ‘1’ given if member of a union, ‘0’ otherwise.

Size of urban area : The city size of the ith person.

Children: The number of children under the age of 18, for the ith person.

Industry and occupation: Binomial variables given for industry occupation groups consisting of: agriculture, mining, construction, manufacturing, transportation, communication, utilities and sanitary services, trade, finance-insurance-real estate, private households, business-auto-repair services, entertainment-recreation services, medical services, educational services, other professional services, forestry and fisheries, public administration, and armed forces, with trade held as the reference group.

Level of education completed: The highest level of schooling completed (in years).

The variables noted in the previous paragraph are hypothesized to influence the gender wage gap, either because of theory or past evidence. Throughout history, women tend to earn lower wages than men, either because they spend less time in the workforce due to family duties and childbirth, or because they traditionally have less years of education than men, though this is changing.

Theoretically, the more experience workers have, the higher their wages. Because the data on actual experience for each individual is difficult to obtain, an equation for potential experience is used. As potential experience increases, wages are expected to increase. In many instances, potential experience may be overstated for women, because it does not take into consideration the time women leave the labor force due to pregnancies and/or child rearing. The number of children the ith person has is included to help adjust this overstatement. As the number of children a woman has increases, the gender wage gap is expected to be greater due to less work experience

The marital status of a woman sends employers various signals. For most, a married woman tends to have lower wages, either because of the increased probability that she will eventually leave the labor force in order to start a family or that she has left the labor force in the past in order to start a family, decreasing her years of experience. Furthermore, men who are married tend to send a higher signal than single men to employers, resulting in higher wages for married men.

The number of hours worked in a typical week is expected to have a positive influence on a person’s wage, because theoretically, the more hours a person works, the more he/she gets paid.

In worker class (government versus private), the effects on a person’s wage is ambiguous. There are not many literary sources that favor one sector over the other. Both private and government positions offer low and high waged jobs.

The geography or region a person works at is likely to increase or decrease the wage as seen from past studies. Oaxaca (1973) found that both men and women living in the South earn less than those living in the Northeast, West, and Midwest.

Union membership is expected to have a positive effect on a person’s wage. Unions push for higher wages and better working conditions as well as equality among wages (Oaxaca).

As years of schooling completed increases, a person’s wage is likely to increase also. Theoretically, the more schooling, the more prepared and knowledgeable a person is, making he/she more valuable to the employer.

Model

Though many authors choose to use the Oaxaca wage decomposition, this study is modeled after Studenmund (2001). Studenmund (2001) uses an understandable linear wage equation to deduce unexplained differences between male and female wages. Furthermore, Studenmund thoroughly explains and simplifies how the model is set up and analyzed.

The wage equation:

lnWagei = B0 + B1Di + B2Xi + Ei

Di = A binomial variable representing whether a person was male or female

Xi = A vector of factors influencing a person’s wage (independent variables)

Although Studenmund does not log the wage in his decomposition, many of the other authors do in order to show a percent change. Observing Di will reveal if a discriminatory wage differential exists between men and women. When Di is being observed, all other independent variables are accounted for and held constant, leaving only the unexplained wage gap.

Econometric Issues

Because many of the independent variables mentioned above correlate to one another, there are some econometric problems that are likely to arise in this model. Even if perfect multicollinearity does not exist, imperfect multicollinearity may exist. The years of schooling completed affects a number of independent variables both directly and indirectly such as: potential experience (age-years of schooling-6) and type of job (physical labor is associated with those who have less years of schooling). Marital status is also correlated to children and number of hours worked a week. Women who are married have a higher probability of having children and working fewer hours a week due to household responsibilities.

Heteroskedasticity may pose a problem in this model. Because this is a cross sectional model, the possibility of error term variances fluctuating is highly probable. The variance for potential experience is most likely not constant. Those individuals who are younger could either have minimal potential experience because of extended schooling or could have a higher amount of experience, if they start work right out of high school. This variance in potential experience would also affect the wage variance between ages.

Data and Empirical Results

The data used in this study comes from the Bureau of Labor Statistics (BLS) Current Population Survey (CPS). The CPS gathers monthly information from a sample of 60,000 households and uses this information to report on the nation’s unemployment and employment “classified by age (over 16), sex, race, and a variety of other characteristics” (United States). The data for this study is drawn from the February 2002 CPS, and individuals are filtered out based on certain characteristics, resulting in a sample size of 4,394 people. Individual observations filtered according to specific variables or characteristics may be downloaded through the United States Census Bureau’s and The Department of Labor’s Data Ferrett[1] system.

Again, the purpose of this study is to see whether a gender wage differential exists in the labor market, assuming various demographics and characteristics that influence a person’s wage are held constant. The sample includes 4,394 observations, and because the data is a comparison between various individuals, the data is cross-sectional. Only full-time, hourly waged workers are included in this model. The dependent variable is the log hourly wage of the ith person. The independent variables listed below are those suspected to have an influence on a person’s wage.

Gender: Binomial variable of 1 given for male and 0 for female.

Potential experience: Because potential experience is hard to calculate for each individual person surveyed, a standard estimation equation is used. It is classified as age minus years of schooling minus 6.

Marital status: Binomial variable of 1 given for a person who is married (including spouse present, spouse absent but not separated), and a 0 is given for a person who is not married (including widowed, separated, divorced, single, and never married).

Number of hours worked (week): The number of hours worked in a typical work week at a full-time job (hours greater than or equal to 35).

Worker Class: Binomial variable of 0 given for a government worker (state, local, federal), and a variable of 1 given for a private worker (profit and non-profit).

Geography region: The region was split into separate variables so that analysis based on binomial variables could be done.

Midwest: Those residing in the Midwest were given a binomial variable value of 1, all else given a 0.

North: Those residing in the North were given a binomial variable value of 1, all else given a 0.

West: Those residing in the West were given a binomial variable value of 1, all else given a 0.

South (left out of regression equation): Those residing in the South were given a binomial variable value of 1, all else given a 0.

Union member: Binomial variable of 1 given if the ith person was a member of a union and a 0 given otherwise.

Size of urban area: The Data Ferrett extraction tool does not give specific city size values but rather ranges of the city size (ex. 100,000-249,999 people). Therefore the average of the range was taken for each observation so that the model may be less susceptible to error. The values used for city size include:

0: Not in a metro area

174,999.5: 100,000-249,999 people

374,999.5: 250,000-499,999 people

749,999.5: 500,000-999,999 people

1,749,999.5: 1,000,000-2,499,999 people

3,749,999.5: 2,500,000-4,999,999 people

5,000,000: 5,000,000 or greater

Children: The number of children under the age of 18, for the ith individual.

Industry and occupation: Binomial variables are split into separate variables and then given a value of 1 if the ith person works in that industry and a value of 0 otherwise (Set up like the Geography region variable). Variables consist of: agriculture, mining, construction, manufacturing-durable and non-durable goods, transportation, communication, utilities and sanitary services, trade (wholesale and retail), finance-insurance-real estate, private households and personal services, business-auto-repair services, entertainment-recreation services, medical services including hospitals, educational services, other professional services, forestry and fisheries, public administration, and armed forces. The variable Trade is left out of the regression model to avoid multicollinearity and to use as a comparison.

Years of Schooling: Values obtained from the Data Ferrett were given in terms of highest level of educational attainment and not in terms of years of schooling. Therefore, based upon the average student that starts schooling at the age of 5, values were calculated as an equivalent to the degree given.

Years of schooling Education level attained

6. Less than Elementary

9 Less than H.S (7-11)

12 H.S diploma or GED

13. Some College, no diploma

14. Associate Degree

16 Bachelor’s Degree

18 Master’s Degree

21 Doctorate Degree

Data Problems

Data obtained from the Data Ferrett program did not always provide the exact variable values needed for the study; therefore, variables were re-coded and/or estimated. The variable “years of schooling completed” was not given in the form of a number but instead was given based upon level of diploma achieved. Therefore, the “years of schooling” variable was re-coded based upon averages (see “years of schooling” variable). Potential experience was not available through the Data Ferrett, and so values were calculated via Microsoft Excel. The equation used for potential experience was age minus “years of schooling” minus six. The “city size” variable was also given in ranges, and these ranges were re-coded to the mean of each range (see “city size” variable).

After the Data Ferrett had filtered out individuals based upon the variables listed above, there were two observations without an hourly wage listed. These observations were removed from the sample (sample size reduced from 4396 to 4394) .

The data included a sample size of 4,394 individuals. There were 1,937 women and 2,457 men in the sample. The average age of the individuals in the sample was 36.2 years old. The Northeast had 883 people in the sample, the South had 1,165 people represented, the West had 1,200 people represented, and the Midwest had 1,146 people represented in the sample. The average number of children for the ith person was 1. There were 714 people in unions and 3,680 people not in unions. There were 1,924 people married while 2,470 people were not married. Government workers consisted of 579 people while 3,815 people were employed by private organizations. The average years of schooling completed was 12.5 years, and the average years of potential experience was 17.7 years. The average city size was 1,718,861 people. In depth descriptive statistics of the data can be found in Table 1: Descriptive Statistics.

Empirical Results

The hypothesized sign of “hours worked” was positive, because the more hours a person works, the more he/she earns. All the geographical regions were expected to be positive as compared to the South; the South is generally characterized by farming and industries that typically are classified as low-waged and low-skilled. The “children” variable was expected to be positive for males, because if a male has children, it generally sends the signal to employers that he is responsible. Likewise, if a male is married, it sends the signal to employers that he is responsible. However for women, the expected sign of “children” is expected to be negative. Women who have children typically lose time in the labor force due to pregnancy absences, decreasing a person’s wage (Oaxaca). Unions push for higher wages and better employment benefits, making the hypothesized “union” variable positive. “Years of schooling” is hypothesized to be positive, because if you have more years of schooling, you are more skilled, more knowledgeable, and it sends a positive signal to employers. “Potential experience” should be positive, because as a person gains experience, he/she is more knowledgeable and capable in that field of work.

Originally, the model was set up with the variable “age” included as an independent variable. The variable “industry” was set up as one variable and was not re-coded into independent sub-variables, and the “city size” variable was in terms of dummy variables (ex. 1 given for city size range of 100,000-249,999 people). OLS regression output can be seen in Appendix 1: Preliminary Model. Surprisingly, “years of schooling” affected the ith person’s wage negatively at -5.27%, and “potential experience” affected the ith person’s wage negatively by -11%. All the variables except “years of schooling” were significant at the 10% level, which was surprising because years of schooling should be an important factor in a person’s wage. The variance inflation factors for “age,” “years of schooling,” and “potential experience” were extremely large, hinting that multicollinearity was an issue.

The model was re-structured in an attempt to fix the problems stated above. “Age” was removed as an independent variable since it was included in the “potential experience” equation. Moreover, theoretically, as age increases, potential experience will also increase. The goal of removing “age” from the model was to reduce or eliminate multicollinearity from the model. “Industry” was re-coded into separate sub-variables so that each type of industry could be analyzed based on a dummy variable value of either 0 or 1. “City size” was re-coded into actual values, based upon the mean of the city size range, instead of using multiple dummy variables. By recoding these variables, more specific and accurate analysis could be achieved. The results of the OLS regression are shown in Appendix 2.

A few of the significant variables that affect a person’s wage are shown below:

|Variables |β |T |V.I.F |

|Gender |.107 |8.492 |1.344 |

|Male = 1 | | | |

|Northeast |.109 |6.609 |1.497 |

|Midwest |.0786 |5.215 |1.498 |

|West |.0826 |5.527 |1.514 |

|Children |-.00343 |-.552 |1.736 |

|Union |.151 |9.413 |1.204 |

|Marital Status |.0766 |5.313 |1.750 |

|Years of Schooling |.057 |23.54 |1.071 |

|Potential Experience |.00723 |13.828 |1.040 |

|City Size |1.844E-08 |6.621 |1.106 |

|Worker Class |-.0552 |-2.194 |2.477 |

|Private=1 | | | |

The wage differential between men and women, holding all other variables constant, is around 11%. That is, males are likely to make 11% more than women, holding all other variables constant. Those who are in a union are likely to make around 15% more than those who are not in unions. With one more year of schooling, a person’s wage is likely to increase by 5.7%. Those who live in the Northeast are likely to make around 11% more than those in the South; those who live in the Midwest are likely to make around 7.9% more than those in the South; those who live in the West are likely to earn around 8.3% more than those in the South. Those who are married are expected to have a 7.7% higher wage than those who are not married, and those with one more year of potential experience are expected to have a wage increase of 7.2%. These results are all significant at the 1% level and are consistent with theoretical belief.

Most variables are highly significant in regards to the t-statistic at the 1% significance level. However, whether a person’s wage is affected if they work for a government office or a private office is insignificant as well as the agricultural, private household, public administration, entertainment/recreational services, and other professions sector of the “industry” variable. The variable “children” is also insignificant at the 1% significance level but has a negative co-efficient as predicted. Each variable passes the variance inflation factor test of less than 5, fixing the problem from the preliminary model. Furthermore, the “years of schooling” and “potential experience” variables are now positive and significant at the 1% level.

The model has an adjusted R2 of .34, meaning that the independent variables predict 34% of the fluctuations of the log wage variable. The F-statistic in this model is 77 and the critical value is 1. Therefore, the null is rejected and the overall fit of the model is significant. The model has a Durbin-Watson of .645 with a lower critical value is 1.53 and an upper critical value of 1.83, assuming positive auto-correlation. Because this is not a time series model, auto-correlation generally should not be a problem. The traces of positive auto-correlation are due to the arrangement of the hourly wages listed in ascending order.

Heteroskedasticity with the variable “potential experience” was initially thought to be a potential problem. Theoretically, those who have a low potential experience could be making a wide range of hourly wages. Those who are young and begin working right after high school may make a low hourly wage. While those who are young and have completed some level of college or higher education will be making a high wage. As age increases so does potential experience and wage. The variance of wages is likely to decrease in size. This is graphically seen in Appendix 3: Heteroskedasticity Graph 1. The Park test was conducted to test whether the presence of heteroskedasticity existed. The test resulted in a t-stat of 4.441, leading to the rejection of the null at all levels of significance and the conclusion that there is heteroskedasticity. This can be seen in Appendix 3: Heteroskedasticity Park Test.

One fix to heteroskedasticity is weighted least squares, in which case the potential variable causing the problem, in this case “age” since it is essentially quite similar to potential experience, is divided throughout the entire OLS equation. Appendix 3: Weighted Least Squares shows the results of this test. Potential experience increases in significance to 14.151, and the coefficient states that if potential experience increases by one year, the wage will go up by .75%, which is not that great of a change. The rest of the variables are quite similar and no variables lost significance.

Conclusion

The hypothesis that an unexplained gender wage differential still exists was verified by the results of the tests based on the February 2002 Current Population Survey. Overall, when the independent variables were held constant, men’s wages were still around 11% higher than women’s wages. Traditionally, when women decide to get married and start a family, they take a break from the labor force, therefore lowering their on the job training, becoming less valuable to employers. Because most women take this path, a woman entering into the labor force automatically sends a negative signal to employers based upon stereotype. These negative signals as well as various other social stereotypes could be the cause of this unexplained wage gap. The results also indicate that various factors such as union membership and increased years of schooling dramatically influence a person’s wage. Though many factors were listed as determinants of a person’s wage, there are still many factors that are not included, either due to inaccessibility or ignorance.

Because the study was based on a sample of only one month, future studies could incorporate a sample of multiple months in order to gauge a more accurate wage differential. Breaking down the industries into inter-industries and occupations and then analyzing the gender wage gap would also be an interesting future study, especially since literary studies have shown that inter-industry wage gaps are quite large. The true value of the gender wage differential will never be completely accurate. There are too many factors, specific personal and job traits, and social issues that influence a person’s wage. However, with further study on this issue, people will at least gain a better understanding of the causes that most influence a person’s wage.

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[1] The Data Ferrett is an electronic “search system [used] for extracting and tabulating data across heterogeneous statistical data sources” (Data Ferrett). In this project, the time period for a specific CPS survey was chosen followed by specific variables that each observation needed to have. The filtered out data was downloaded into an Excel file.

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