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Research Process: Varying Wages among Today’s Workers

Today’s workers’ wages for most individuals is defined as the money that is paid or received for work or services, as by the hour, day, or week (Dictionary.com, 2006). In most cases and to add to that definition, most individuals believe that the more educated, experienced, and overall qualified a candidate is for a given position increases a person’s wage earnings. Minimum wage is the lowest wage payable to employees in general or to designated employees as fixed by law or by union agreement (Dictionary.com, 2006). Many individuals believe minimum wage is for entry level, minimal to no experience or education workers. Statistics show and prove that in today’s professions, wages vary among the old and young, the married and single, the experienced and inexperience, and the educated and uneducated; as well as differences among the races and genders. The law can set a minimum wage, but whether a worker is compensated for what they bring to the organization, versus a comparison of youth, status, or gender to their coworker is definitely debatable.

Varying Wages: Defining the Problem

Varying wages among today’s workers is the difference between the amount worker “A” receives, versus the amount worker “B” receives who is occupying the same place and conducting the same work and responsibilities. However, worker “A” is a recent four-year college graduate, newly married, with no actual work experience outside of the three months of internship completed over the previous summer and is hired with a salary of $60,000 per year. Whereas, worker “B” has a two-year college degree from 20 years earlier, 18 years of experience in the field and holds the same position for $20,000 less than worker “A.” The debate is not the quality of work either individual can or does provide, but the varying wages does not appear to coincide with the rewards society says stability and hard work grants.

Varying Wages: Explaining the Problem

Varying wages among today’s workers is a problem that requires research not only because the situations are biased and unjustified, but also because in some cases wages are being dispersed based on personalities and characteristics of a person who has nothing to do with the work at hand. Statistical data from the U.S. Census Bureau shows there to have been a widening in the gap between men and women wages, impacting women negatively while men’s wages remained the same. Between the years of 2002 and 2003, women’s median earnings declined by 0.6 percent (Andrew & Lips, 2008). This is a concerning issue across the employees of the world as all workers expect to be paid for the quality of work and service they are able to provide to a company. Basing wage on anything other than the pure results can be considered unethical and will cause many businesses to over pay for resources that cannot get the job done, while discouraging and ultimately losing those who perform well.

Varying Wages: Questions to be answered

“The distribution of income, the rate of pay raises, and the mobility of employees are all crucial to our understanding of labor economics. But though there is much research on the distribution of wages across individuals in the economy, wage differentials within firms remain a mystery to economists” (Winter 2008). Many assumptions or preconceived notions around the varying wages in the world exist, with the top issue of race being number one on many individual’s list. However, the preliminary statistical data does not prove this factual, while also not disproving the claim. Research will set out to answer who the varying wages are affecting, what factors are leading to the varying wages, are these factors ethical, and what can be done to eliminate any factors that are deemed unethical.

Operational Definitions

During the research there will be several variables available to be tested. They will be: annual wages, industry, occupation, years of education, southern resident, non-white, Hispanic, female, years of work experience, married, age in years, and union members. This will help identify the problem between the wages of the workers. The level of measurement for each variable will identify the responses in order to identify the issue. The measurement used is nominal. “In nominal measurement the numerical values just “name” the attribute uniquely. No ordering of the cases is implied” (Research Methods Knowledge Base, 2006). The measurement scale for each variable will be from 0-5 with some variables the option will be either a 0 or a 1. The responses for other variables will be based on the individual annual wage, years of education, years of work experience, and age in years with no answer being better than the other.

A Plan to Resolve

In order to resolve this unfortunate bias among salaries, a further collection of data and analysis will need to be completed to understand the organization’s history with pay wages for similar positions. A comparison of those individuals in the same positions will be completed in order to determine whether education, experience, marital status, race, or age play a factor in the current annual wages. Initially and based on the data of wages provided, the only 3 workers in a construction position have varying salaries as the highest paid makes nearly $20K more than the lowest paid.

Review of Literature

Peer-reviewed article Blacks in Government (BIG) Educate the Public in Training Sessions and Banquet, it discusses the Equal Pay Act (sex based wage discrimination) that was established to protect females. Additionally, the original Title VII of the Civil Rights Act of 1964 originally covered race, national origin, color, and religion then later sex was added (Neal-Vincent, May 2008). This article is a good example of showing how discrimination in the workplace is something that will not be tolerated.
Another article simultaneously incorporates two sources of selection bias in the black-white wage equations by demonstrating the biases due to an individual’s propensity to be in the labor force and the firm’s hiring practices are important in determining the black–white wage differential and failure to account for both biases will result in inaccurate estimation of the black–white wage differential. Research found that adjusting for double selection bias in the wage equation, the black–white female wage gap is 26% larger than the black–white male wage gap and 12.1% larger when adjusted for a single selection bias. The results seem to suggest that at the macro level, the enforcement of policies related to racial issues in the labor market will likely lead to a reduction in the black–white wage gap (Baffoe-Bonnie, March 2009). This article shows that even in today’s business firms discrimination still exists between race and gender.
An additional article goes into detail of the different positions and salaries compared to gender and race. This article shows that discrimination is not limited to the corporate level, but even in the government and the courts system in which laws are ruled on and enforced, discrimination exists. One must remember to not just compare job positions and salary. Certain factors must be considered when comparing a job position and salary of employees. These factors can account for some differences in position to pay but accompany blatant times in which employees with equal experience and education were paid different wages based solely on race or gender.

Sampling Design

The design of the sampling used in this research will be nominal "Stratified Random Sampling" as the samples of the population will be chosen randomly based on the industry variable of “other.” “Stratified random sampling, as its name implies, involves a process of stratification or segregation, followed by random selection of subjects from each stratum. The population is first divided into mutually exclusive groups that are relevant, appropriate, and meaningful in the context of the study”. (Sekaran, 2003, p. 272)
Sample Data
The population comprising the samples chosen in our research was one hundred workers from different variables such as annual wages, industry, occupation, years of education, southern resident, non-white, Hispanic, female, years of work experience, married, age in years, and union members. Of these variables, the Stratified Random Sampling variable chosen was “industry-other” and 40 random samples were chosen. From these random samples another Stratified Random Sample was chosen which was “occupation-professional” which narrowed the samples down to a total of 11. This sample size is sufficient for our research since we are using Stratified Random Sampling and want to only “professional” workers across non-specific occupations. Of the population data set, we were able to narrow down samples to our specific research criteria.
From these samples we can derive that the following statistics are present among the samples:

Wages – $14,476 - $83,601
Years of Education – 9 - 18
Southern Resident – 2
Non-White – 1
Hispanic – 1
Female – 6
Years of Work Experience – 3 - 39
Married Yes – 7
Age in Years – 21 - 57
Union Members Yes – 6

The possible sources of bias or error that could be present in this type of sampling could be our specific selections of random variables, miscalculations in statistical values presented, and a presumed bias from the reader based on other information they were looking for not specific to our variables chosen.
Data Collection

Wages from 2005 across manufacturing, construction and other occupations with varying positions illustrates the inconsistency among wages earned compared to education, experience, or age. An initial conclusion can be made insisting no formal or structured guidelines around salary potentials or earnings in a given career.

Primary data collection methods

Primary data is information obtained by the researcher, and the method use to conduct the research is exclusively that of the researcher. Individuals can conduct research through questionnaires, interviews, focus groups, panels, unobtrusive measures or observations. Focus groups are composed by a group of expertise on the topic of the research. Panels are also composed of a group of individuals but are randomly chosen and have no expertise on the subject for research. Another primary method is unobtrusive measures where the data obtained is from a primary source that does not involve people. “The key point here is that the data you collect is unique to you and your research and, until you publish, no one else has access to it” (Brent, 2008).

Ethical Concerns with Data Collection

Ethical concerns with data collection include protecting the privacy and confidentiality of individuals who have agreed to participate. The participants need to give their consent, and the researcher has to provide accurate information and not bias personal beliefs. The researcher has to abstain from asking demeaning questions, coercion, and appear deceiving. Additionally, the participants should not be exposed to a hazard environment, stress and can withdraw from the research at any time. “Many universities have a “human subjects committee” to protect the right of individuals participating in any type of research activity involving people”


Andrew, W., & Lips, H. (2008, September 10). Gender & The Wage Gap: Internet Data Sites & Information Sources. Retrieved February 8, 2009, from Pay Equity and Women's Wage Gaps by Dr. Hilary Lips: http://gstudies.asp.radford.edu/sources/wage_gaps/wagegap.htm#backslide

Baffoe-Bonnie, J. (March 2009). Black–White Wage Differentials in a Multiple Sample
Selection Bias Model. Atlantic Economic Journal, 37(1), 1-16.
Minimum Wage. (2006). Retrieved February 8, 2009, from Dictionary.com: http://dictionary.reference.com/search?q=minimum%20wage&db=luna

Neal-Vincent, J. K. (May 2008). Blacks in Government (BIG) educate the public in training
sessions and banquet. Mississippi Link, 15(19), 4.

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