Does Attaining A College Degree Imply Higher Lifetime Earnings?

Going to college and attaining a degree is not an easy task. There are many reasons for people to follow this path, but I will focus on the earnings potential that a college graduate gains. This begs the question, is there a wage gap between college graduates and non graduates? I have asked this question for a personal reason. I came back to college so that I could finish my degree (peace of mind), and to have greater flexibility within the labor market and the potential for higher lifetime earnings. This investigation will begin with the overall advantages, if any, that a worker receives by attaining a degree. Next, I will explore the general effects that higher educated workers have on income inequality. Finally, it is worth considering the effects of economic recession or growth on wage determination. The overall theory associated here is that the human capital model suggests that higher educated workers will be more productive; hence, they should have a higher per capita income. I revisited data that I looked at is based on the 2000 release of the Bureau of Economic Analysis, Bureau of Labor Statistics and the Current Population Survey. I focused on 152 metropolitan areas to constrain this limited study. The final theory that is significant is the production function model. This is relevant regarding the unemployment rates in a given metropolitan community. The idea is that if there are more unemployed workers in a given area, then the per capita income would be lower. The purpose of this study is to examine the relationship between per capita income and factors such as the educational levels of the inhabitants and the unemployment levels.

Do workers gain any type of advantage as far as wage considerations with the completion of a college degree? “Individuals obtain schooling to leave unemployment more quickly and to climb the wage ladder rapidly through job-to-job mobility — that is, to speed up job shopping. Education reduces firms' monopsony power in the wage determination by improving workers' mobility. As a result, the wage distribution shifts rightward with aggregate schooling. (LABOUR)” It is apparent that individuals that are more highly educated are more likely to have a bargaining chip when negotiating wages with employers. This quote from the LABOUR Journal suggests that without higher education, the firm has more power to determine the individual workers’ wage rate. Phew…I guess coming back to school was a good thing!

With this being said, does the pool of educated workers effect income inequality in the labor market? “When borrowing for education is difficult, lack of a college education could mean that one is either of low ability or of high ability but with low financial resources. When government programs make borrowing or lower tuition more affordable, high-ability persons become educated and leave the uneducated pool, driving down the wage for unskilled workers and raising the skill premium. (Journal of Public Economics)” This is significant especially in the wake of globalization and its effects on the labor market. Globalization has introduced outsourcing as a profit maximizing tool for many firms that can potentially lead to structural unemployment. The nature of the jobs that are outsourced is relevant because of the segment of the market that is affected the most. Generally speaking, most of the jobs that are outsourced are of a manufacturing nature and these types of jobs usually are held by workers that do not have college degrees. Therefore, the wage gap between the uneducated and the educated is enhanced. It has become necessary to get a degree. The glory days when the family had one bread winner who worked at the factory are over. This argument, though, has another side to it. According to Bhagwati, the integration of richer nations with poorer countries does not depress wages. In chapter 10, he explains that in 1980, real wages of American workers were stagnant and the prices of labor intensive goods rose relative to the prices of the set of other goods in world trade. He argues that studies showed a positive result relating to outsourcing of labor intensive goods—real wages increased. (Bhagwati, chapter 10) This leads to one conclusion: Outsourcing reduces the wage gap between the educated and uneducated workers.

So what happens to wages during times of recession or expansion? “Asymmetry in the cyclical behavior of the real wage is widespread across the U.S. economy. The reduction in the real wage during recessions appears pronouncedly larger compared to the increase in the real wage during expansions in many industries. Across industries, price inflation increases faster compared to nominal wage inflation in the face of higher demand variability. Price flexibility moderates the increase in the real wage and output growth during expansions. In contrast, prices appear more downwardly rigid compared to the nominal wage in the face of demand variability. Price rigidity exacerbates the reduction in the real wage and output contraction during recessions.(Empirical Economics)” Wow!! I could not have said that better myself. What are the implications of this in regards to educational attainment then? It is apparent that during times of recession, unskilled and relatively uneducated workers are going to experience the greatest hardships for two reasons: 1. they will have less flexibility in the labor market to find new jobs and 2. They are more likely to have lower wages to begin with and will less likely lose purchasing power. During times of economic recession, it appears that workers lose some of their ability to bargain for higher wages and therefore the skill levels of the individual become crucial to earnings potential.

Last semester, Prateek Sangal and I ran a regression analysis using data from 152 metropolitan areas in the United States regarding educational levels, population, and unemployment rates to determine the overall effects that having a degree would mean on income.

Model Specification:

According to the conceptual model suggested by the theory, the per capita income in a metropolitan area can be specified as a function of the percentage of people with a college degree and the percentage of people unemployed. The conceptual hypothesis can be stated as follows: Per capita income=f (% with a college degree, %unemployed). Hence, per capita income is the dependent variable of this model, which will indicate the effect of the independent variables on income earned by a population in a given metropolitan area. The following is the model used for empirical analysis:

PI=α0 + α1(deg) + α2(unemployed + ε

“PI” is the dependent variable which is the per capita income in the different metropolitan areas and is in actual dollar amounts. “Deg” is the independent variable which is the percentage of people over the age of 25 with a college degree. “Unemployment” is the independent variable which is the percentage of people that are not in the workforce in the given metropolitan community. As the theory suggests, the relationship between “deg” and the dependent variable is expected to be positive because as the percentage of people with a college degree increases, the human capital model would suggest that per capita income should rise. The relationship between “unemployment” and the dependent variable is going to be negative because as the percentage of the population unemployed goes up, then per capita income should go down. The descriptive statistics for the variables thus obtained is as follows:

Name / N / Mean / St. Dev / Minimum / Maximum
PI / 152 / 25434.32 / 366.18 / 15071.00 / 43107.00
unemployment / 152 / 3.74 / 1.03 / 1.50 / 7.80
Deg / 152 / 14.55 / 4.00 / 7.10 / 26.20

Regression Results:

This section aims at interpreting OLS estimates on the coefficients of the variables in this model and to test their significance using the appropriate T-Tests. The explanatory power of all the independent variables acting together to explain the variance in the dependent variable is estimated by looking at the R2 value. The table below illustrates the results of the regression:

Variables / Expected Sign / Estimated Coefficients
Intercept / N/A / 24444*
(16.20)
Deg / Positive / 389.36*
(6.29)
Unemployment / Negative / -1198.42*
(-0.94)
N / 152
F-stat / 35.02*
R2 / 0.4151
Adjusted R2 / 0.4033

In the above table (*) is for the 0.01 level of significance [significant if: sample t>critical f]

From the above table it is clear that the F-stat is significant so the implication is that the null hypothesis is rejected that all the coefficients are collectively statistically equal to zero. The r squared value of 0.4151 explains that approximately 42% of the variation in the movement of the dependent variable is explained. It is also worth noting that the variables “deg” and “unemployment” are significant at the 90% level and the coefficients for both have the expected signs. The variable of the most concern was the “deg” variable and is worth noting that if the percentage of people with a college degree goes up by 1% than the analysis would imply that per capita income would go up by 389.36.

In conclusion, it is clear that workers with higher rates of education receive higher wages, overall. They are more likely to have better bargaining power with employers and are less likely to be affected in negative ways during economic downturns. Thanks for everything Dr. King, I appreciate the extension that you gave me and I hope that you have a great holiday season. See you in January.

1