In all forms and colors of societies, the pursuit of acquiring a job is a prominent one. Jobs are necessary in the limited time we have in this world. However, jobs are not all equal and it highly influences what kind of standard we can provide for ourselves and our loved ones. It is because of the wage that the job pays that determines your living standards. How much you are paid is a signal of the value society places on the skills/service you provide.
Little would argue against the fact that, education level and experience are the two most important factors that contribute to someones wage is , but does it stop there? Or are there other characteristics that influences wages as well?
In this post and future posts I will be looking at data from from the census population survey (CPS) for March 2015 for the United States. Our interest here is trying to consider characteristics that can explain wage differentials. The reason this is important is because it is crucial to know whether characteristics that are endowed to us at birth might be detrimental to determining our wage.
We will be focusing this post by looking at averages, and I will emphasize why looking and comparing averages gives an unclear picture of reality. I will begin by providing some summary statistics for this sample.
Table 1 tells us that the average hourly wage for our sample is $23/hour, the average age is 39, the average years of education is 13, and average experience is 27 years.
Table 2 (below) tell us more information about the sample. For example, there are 150,907*0.443= 66867 females and so on (Column 5). Column 5 displays how many people are in the data based on gender, race, marital status, the number of children under 18 a person has, whether if someone is from the south or north, and whether he/she is a member of a union, and race.
For the purposes of this post we are going to examine Gender wage differentials. We are interested to see whether being a female or male affects your earnings.
If we look at the table above, it seems that being a female is associated with a lower average hourly wage than that of a male. The female earns on average $21 where the male earns $25. In other words, females earn 0.84 cents for every $1 a male makes. But this is a broad statement, isn’t it ? Think about it! we are not really specifying anything about this sample. Could it be that men are just more educated than females, and that is why they receive a higher wage? We need a more accurate look to start making statements. Let us consider the sample again but this time lets limit our search to males and females that hold Bachelor, Graduate, and High school degrees.
Let us start off with the examining the average wage between males and females at the bachelor degree level.
In table 4, we can see that females with bachelor degrees earn $27 compared to males with bachelor degrees who earn $33 . This is even worse than the average we first computed (table 3). For Bachelor degree holders females earn 81 cents on the dollar compared to a male.
Let us continue by looking at the average wage between males and females at the Graduate level, (i.e. +18 years of schooling). Table 5 below surprisingly displays output on the wage differential between males and females and finds that it shrinks at the masters and doctorate level.
The wage differential now is much smaller at this level of education when comparing between genders. Females earn 97 cents for every $1 a male earns.
Let us now look at High school educated males and females (12 years of schooling). It is here where we find the largest wage gap . Females earn 75 cents for every $1 a male earns at this level of education.
This is important to realize, first for policy makers and for people with false perception of reality and no data to back up their assumptions. Gender wage differential does exist, but it is highly dependable on many factors. As I have demonstrated it seems that among High school and Bachelor degree holders the female/male wage gap is largest and starts to get smaller as the level of education gets higher.
It is vital to mention that the magnitude of the gap is not very accurate. Since there are other unobserved characteristics that might be causing these numbers and this data and method (looking at averages) does not take these into consideration. For example, it does not take majors the types of different degrees pursued by either males and females into account. Nor does it consider whether females might be more inclined to attain certain jobs that might be causing a large supply of females in a specific labor market. These issues could potentially be behind lower wages, regardless of having the same level of education.
Consider the graph below as an example. Many females find it satisfying working in educational institutions because the work time suits their preferences and their ability to work and finish before 3 pm,give or take, which allows them to be home with their families. This preference is shared by many females and sometimes causes unintended market consequences.
If a large number of females have the same preferences in jobs that have flexible hours. Then this causes a flood of females in that labor market. In the simple graphical representation (Figure 1.), we can imagine that a flood of females in a certain job can cause the wage rate to go down.
The implication here is not to say that gender wage discrimination does not exist, and that some other factors are causing a lower wage rate for females. What I am trying to portray is from a policy stand point, it might be more ideal to create more jobs that are more favorable to women’s preferences, in the effort to induce females to disperse to a wider scope of jobs. Rather than enforce wage floors that goes against market forces. Because enforcing prices can cause unintended consequences, in one extreme a higher unemployment.