Qualitative cost-benefit of a college degree

Written by  Thursday, 14 April 2016 12:11

Recently, students have been paying huge amounts of money to attend a college but earned less upon graduation (Arai, 1998).  Such trends have caused many observers to question if a college degree is a worthwhile investment. To be precise, college students endure the following expenses: supplies such as text books, tuition fees, food, room and board among others. Supplies, tuition fees and books are the direct costs of education, but board and room are indirect costs as they are incurred to sustain a subsistence level of education.  Besides the above, opportunity cost is one of the most important costs as these students forgo earnings while in school. While it is categorized under indirect costs, it accounts for nearly 40% of the total college degree costs (Reynolds et al. 2007).  In addition to the above costs that students endure, a college education is also associated with social costs and non-market costs. For instance, the social costs are a result of philanthropies and government subsidies. Non-market costs related to college education include job-related stress, marital instability, alienation and destructive social protests.

Economists have examined the earnings of workers with bachelor’s degrees, workers with high school diplomas and workers with associate’s degrees (Reynolds et al. 2007).  The findings reveal that despite the costs, a college degree remains a valuable investment. A key reason is that a college degree is a relatively valued asset regardless of the doubling tuition fees because the salaries of Americans without a college education have been falling. The outcome has been a premium college wage, which is currently nearing an all-time high. Reports indicate that between 1980 and 2015, employees with a college degree had annual earnings of roughly $50,000 after adjustment for inflation (Reynolds et al. 2007).  Employees with an associate’s degree took home approximately $40,000 while those with a high school diploma were paid $36,000 (Arai, 1998). Economists project that in the next four decades, employees with a college degree will earn an average of 60% more and employees with an associate degree, an average of 20% more than the high school graduates. Research indicates that individuals who pursue college studies “may well have aptitudes, skills, and other characteristics that make them different from those who do not go on to college.” Such differences explain why college graduates are earning almost double of what non-graduates are earning. However, it is argued that even after examining the complete set of costs and benefits, an investment in a college education remains a wise economic decision for students.

These factors are difficult to quantify because a recent Census Bureau report discovered that the number of American students attending college has dropped by nearly half a million between 2011 and 2015  (Reynolds et al. 2007). In spite of studies indicating a better overall quality of life and higher employment rates for college graduates, the past few years have experienced a declining trend in the pursuit of higher education. The sky rocketing costs of college education, worsened by heightened scrutiny concerning its value, presents a multifaceted issue that absolutely lends itself to mathematical modeling. Previously, experts in the academic field have been unable to quantify the value of college education. Interestingly, scientists are currently experimenting with new models to calculating its value. For instance, Stanford, MIT and Harvard are diving into the arena of free online course work leading to alternative approaches to certifying mastery in massively open online courses (MOOCS). Moreover, the Mozilla Foundation, based in Silicon Valley is currently promoting the theory of open badges that aims to demonstrate the mastery of both informal and formal learning. This presents a great challenge in rethinking how to rate and rank colleges today. Traditionally, the rating and ranking were based on an alchemistic combination of hard-to-measure components like campus-based statistics and reputation, among them acceptance rates, endowment and library sizes among others. The issue is that neither of these metrics can directly measure performance once they arrive in college (Arai, 1998). Moreover, while a college diploma is an indicator of what a person has learnt, it is not necessarily a signal of the actual expertise and skills the person is ready to apply.

Variables that contribute to higher or lower salaries

Performance is commonly used to determine an employee’s pay. This approach is commonly known as “pay-for-performance.” If a person is an exceptional performer, then his/her salary increment will take this into consideration and get the employee closer to the higher end of the range (Gratz, 2009). On the other hand, for a low performer, he/she may not see any salary increment. Similarly, if an organization is performing good and follows a profit sharing structure, workers are likely to reap benefits from the organization’s performance. Besides performance, labour unions are also influential in determining a salary increase (Gratz, 2009). Primarily, the labour unions try to influence salaries by affecting or regulating the supply of labour in the market. They practice their influence for allowances and a higher salary via collective bargaining with the representatives of the management. If the collective bargaining efforts do not result in allowances and salary increment, they turn to strikes and other approaches under which the supply of labour is restricted.

Supply and demand are another factor that shapes salary increment. Salary is defined as the price for the services rendered by an employee or worker. Because a company desires these services, it must thus pay a price that will generate the supply, which is controlled by a group of employees via unions or an individual employee (Gratz, 2009). The practical implication of this theory of supply and demand is the invention of “going- wage rate”. Generally, given that something happens to reduce labour supply, like restrictions by a specific labour movement, companies will respond by increasing salaries. The reverse of the situation is predicted to result in a reduction of employee salaries, given other variables, like those discussed below, do not intervene.

This section uses a scientific method to describe in detail how I would design a study to estimate the effect of education, performance, labour supply and unions on wages and earning. It gives a hypothesis of the directions and magnitudes of these factors regarding their effect on salaries. The research methodology is also discussed.  

Hypothesis of the research

Research variables: effects of performance, education and labour supply on wages

In the initial stage of the research, this section proposes the following hypothesis which characterize employees’ salaries in the US

Do those persons with more education, more productive and are involved in labour unions, make more money than those of less ability and less education?

An investigation into the effects of performance, education and labour supply is complex by the selective distribution of these variables themselves. In fact, on average, those who exhibit these variables have greater aptitude scores, have had more pressure for success and came from homes with higher socio-economic status. Given these correlated variables influence wages, disentangling the impacts of these factors from people’s backgrounds will be impossible. To research the effects of the three variables, then, at best, the study will control for extraneous influence and consider that other factors could be potential justifications for wage increment (Westenholz-Bless & Achola, 2007).


The sample in this study will consist of mature males and females (40 to 50 years old), working in a factory, presumably at the peak of their careers and earning power. Most have long completed their formal studies and thus the effects of the research variables have been stabilized. This sample will be used because the data gathered when the samples are in their early twenties might be analyzed in conjunction with incomes, performance and educational levels data gathered at later points in time. Generally, sample participants have higher performance rates than the population as a whole, and they are more focused on entrepreneurship. Nearly one-quarter of the sample population are in each of the academic groups: post-college training, college graduates, college training and high school graduates.


To explore the long-run effects of the three study variables on wages, the sample has been restricted to mature persons. As indicated earlier, the effects of the study variables on persons in their twenties are complicated by the late appearance on the labor market of those with formal education. For instance, medical graduates might not start earning professional until they attain their thirties. One approach may be to collect a sample of older persons today and then look into their past to discover how they performed in the past, as youth. Nevertheless, the data generated from this type of sample is likely to be extremely irregular and challenging to gather thus it will be of little practical value. Alternatively, the research team can locate a uniform data pool collected years ago and attempt to gather uniform data on the present performance, education level and salaries of sample members (Westenholz-Bless & Achola, 2007). Sadly, this approach is biased by the difficulty and impossibility of identifying and locating people. However, this study will be based on a sample gathered in the latter method, which seems to be the most feasible approach.

Statistical issues

There are so many statistical issues to this research project. First, the salaries of the sampled employees can only be estimated on a monthly basis. Of course, this cannot reflect the reality as some people, like those in the agricultural industry; often receive their wages on a seasonal basis. The level of education that employees obtain is likewise a big variable. In most cases, a higher academic level is often associated with a great salary. Of course, this makes a significant difference in salary determinant. The study also wanted to include wealth variable to determine salary into the equation, but this was made impossible due to the limitation in the present data (McIntyre, 2005).

Another statistical issue is that this research has only focused on how the three variables affect salaries of mature employees within an organization. Therefore, the findings of this research might not apply to other employees outside the chosen age bracket (Westenholz-Bless & Achola, 2007). This means that the outcomes of this study will only be applicable within the sampled age group and would not represent the effects of the three variables on the salaries of all employees. The other issue is that the research project has only concentrated on the non-administrative workers of a factory. Therefore, the findings are not applicable to workers in other occupations such as lawyers, teachers, office employees and much more. Hence, the people who refer to this study must understand that the study participants are factory employees. The researcher must exercise some caution because discrepancies between the supervisors’ and workers’ responses could arise from impartial coverage of friendly policies rather than worker ignorance. McIntyre (2005) advised that the researcher should try to limit this possibility by excluding part-time and temporary workers.  


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