it is vital to have an understanding of the determinants of a higher wage hence being able to decide on the future and self-development.

it is vital to have an understanding of the determinants of a higher wage hence being able to decide on the future and self-development.

Introduction

All over the world, millions of people engage in different works ranging from casual to formal employment to earn a living. As employees exchange pay with their skill to the employers, they directly or indirectly drive the economy of the world. There are different forms of pay depending on the type, amount, or quality of work done. Some employees get paid through allowances, wages, bonuses, or even monthly salaries. The money paid to workers enable them plan their financial endeavors for the present and the future times. Besides, different employees have different ambitions in life which include career progression or turnover that is highly affected by the amount of pay they receive. Therefore, it is vital to have an understanding of the determinants of a higher wage hence being able to decide on the future and self-development.

Education plays a bigger role in deciding the type of job one may land. Most formal employment opportunities require individuals with trained skills and knowledge on a particular field. The conventional knowledge is that persons who study STEM (Science, Technology, Engineering, and Mathematics) are likely to earn more than their peers who undertake other courses such as in the field of arts. However, all academic majors are important in driving the economy since every expertise is essential in one or another. According to ### individuals holding STEM degrees are likely to receive less pay than those from liberal majors as technology progresses.

According to several scholars, nations that provide competitive salaries tend to attract the best minds in a globalized economy hence improving their economic growth. It is, therefore, important for policy makers, economic stake holders, and the government at large to understand the factors that affect the amount of wages offered. Similarly, students should be well versed with the knowledge on the salaries different academic majors attract before joining to study and train in them.

Purpose

This study is focused on understanding the effect of an academic major on the wages offered to working citizens.

Research Question

Do engineering majors earn higher wages than business, social science, or humanities?

Research significance         

This research outcomes complements to the existing literature on the earnings to post-secondary education majors. Similarly, evidence from this study presents practical knowledge to policy makers on the distribution of income based on different fields of study by examining the potential causes of heterogeneity in the rates of payoffs by education major. This study can also inform debates over the interventions of governments in addressing market frictions and visible mismatches in the college majors. For example, most governments all over the world are calling for educational reforms in a bid to increase the number of students graduating in STEM fields.  This study also provides guidance to students who often make their educational choices based on limited information on the earnings in the labor market.

LITERATURE REVIEW

This section provides a critical review of previous studies on the relationship between academic majors and the amount of wages received. The implications in the choices of both college and high school majors have attracted several academic literature and modelling in regards to wages. The context of review of the past literary works is set by analyzing scholarly research on the sociology of the labor market. A keen look at studies dwelling on the relationship between college majors and careers is paramount. Similarly, a review of literature on classical concepts such as human capital and exploitation gives a clear understanding on the implications of college majors on the wage distribution scale.

Altonji et al. (2012) in their research paper provide an in depth analysis of theoretical literature as well as empirical modelling of the heterogeneous nature of education and how it connects with specific occupation paths. The scholars’ study develops a model which draws a distinction on varying education choices and their causal effects on wages. Decisions made under uncertainties about ability, knowledge, and preferences leads to different possibilities on the wage effects of various education outcomes (Altonji et al. 2012). An increase in the payoff in one field others held constant, say engineering, increases the number of individuals registering for that particular course. Conversely, the move reduces the number of individuals interested in other majors for example humanities (Altonji et al. 2012). Altonji et al. 2012 report that there are large gaps on wage returns across different fields that attract students with similar grades to institutions of higher learning.

A considerable variation exists in the earnings of college graduates in regards to their field of study. (Arcidiacono, 2014). An agreement to the study findings by Altonji, et al. 2012 and James, et al. 1989 is echoed by Freeman and Hirsch (2008). In their study, the two scholars prove that the number of graduates in a particular college major is in relation to the idea of the occupation and the knowledge of the market payoff in the specific field.  The scholars add that an occupational choice is highly influenced by the skills and knowledge acquired along the course of study. College graduates have more knowledge about careers that are suitable including the amount of market payoffs than individuals who do not attend college (Freeman and Hirsch, (2008). Ransom (2014) reported that occupations of persons holding degrees in specific fields explain the high pays in those majors. However, high pays in the fields of engineering and physics show varying patterns hence cannot be explained by the occupations held by people in those majors (Ransom, 2014).

Kirkeboen et al. (2014) study why people choose different majors and the resulting payoffs to the choices made. The scholars’ research assesses whether people tend to select fields in which they seem to possess comparative or absolute advantage. Different fields have significantly varying payoffs institutional differences and peer groups quality notwithstanding (Kirkeboen et al. 2016). Furthermore, Kirkeboen et al. (2016) provide evidence that individuals have a tendency of choosing fields in which they are having a competitive advantage. Their findings agree with Altonji et al. (2012) by suggesting the presence of substantial heterogeneity in the returns to studying different college degrees depending on selection. By examining the heterogeneity in levels of earnings according to educational choices, Kirkeboen et al. (2016) determine the magnitude of the payoffs and at the same time find that people sort into fields in which they possess absolute advantage.

According to Yao (2019), the gap present across college majors in terms of earnings is wide and substantial. Yao’s research focused on the mode of generating field premiums and the wages gap across college majors. Heterogeneity in cognitive skills influences the college field choices made by individuals. The increase in literacy and numeracy tends to sway people away from less skilled courses such as arts, humanities, and education, towards STEM majors (Yao, 2019). According to Yao, there is gender differences when choosing a major in terms of skill, men have stronger preferences to high skilled majors in STEM than women. The heterogeneous skills of people in the different graduate majors are also reflected in the earnings gap between the majors (Van de Werfhost, 2002). Werfhost provides evidence that numeracy skills are important for the labor market outcomes than literacy skills. Further, Werfhost suggests that the heterogeneity in skills together with the variations in course contents leads to a difference in the career professional choices across majors subsequently influencing earnings. The field premium is, therefore, generated from the process.

According to Yao (2019), there is still large field premiums even after correcting skill heterogeneity. The phenomenal may arise due to a likelihood in the mismatch between college major studied and employment. A similar argument is by Robst (2007) who suggests that occupational mismatched employees receive a less pay than well-matched workers having the same degree field. Therefore, premium differentiation can be as a result of the level to which employees in a field are mismatched. According to Robst, individuals working in fields such as engineering, health professions, and law have a high likelihood of being in a related field to their college majors hence low mismatch levels and, therefore, explaining high premiums. On the other hand, Yao (2019) argues that the skills from such type of majors could be high valued in the market hence the emergence of high premiums. Such premiums could be regarded as occupational labor market rewards available for their knowledge gained in college together with practical skills (Zafar, 2013).

According to several studies, a variation in wages in the labor market could also be as a result of other factors such as race and experience. According to Altonji and Pierret (2001), individuals who have been in a particular field for more years could earn more payoffs than new entrants. This could also lead to variations of earnings between different fields of study taking into account that people with much experience from a major traditionally considered to have low payoffs could be earning more than new entrants in fields considered to have large returns (Altonji and Pierret , 2001). Factors such as gender and race form part of discrimination in the labor market capital inclination (Blau and Kahn 2017).

DATA AND METHODS

This section is concerned with the research design and the methodology employed. The data collection procedure and the variables of the dataset used in the study are explained. Lastly, the data analysis technique is outlined.

Design

The research design used for the present study was a cross-sectional design technique involving different college majors and their variation in payoffs. Differences between the wages in various fields of study are measured through a passive approach. The research uses data at specific times to estimate the heterogeneity in the earnings from the college fields. The findings of the study, therefore, entail being able to predict the amount of salaries a particular college major can influence at a particular time.

Population and sample

The target population consists of employees in the American labor market possessing degree level education. The study uses a data from a general population of American citizens and samples out 50 college majors to make comparisons on their payoffs.

Variables

The primary variables of the research are the undergraduate degree major, the starting median salary, and the mid-career median salary. Other variables include percent change from the starting to the mid-career median salaries, mid-career 25^th, 50^th, and 75^th percentile salary. The mid-career median salary is taken as the dependent variable.

Undergraduate degree: The variable represents a college major chosen by an individual. The dataset was collected on people having employment in the fields of study. This is an independent variable that tries to predict the level of mid-year income of an individual.

Starting median salary: This is the amount of income that individuals working in the particular fields of study averagely receive within the first few years of their work. This salary scale is used to determine the mid-career pay. The variable is in US dollars.

Mid-career salary: The variable represents salaries of citizens earned after about 10 years of work. It is the dependent variable for the present study. The variable is set to be determined by the college major and the starting median salaries. The values of the variables are given in US dollars.

Mid-career 25^th percentile: This is the amount of salaries received in the first quarter of the years in work considered to be the mid-career years (8-15). The variable values are in US dollars.

Mid-career 50^th Percentile: Amount of salaries paid to working individuals to the average years considered to be mid-career years. The variable values are in US dollars.

Mid-career 75^th percentile: The amount of salaries paid to individuals falling in the third quarter of the years considered to be mid-career years. The variable values are in US dollars.

Data Collection

The data employed in this study was secondary data sourced from a public data repository named kaggle.com. The website contains numerous datasets that are publically accessible to students and scholars. The dataset does not contain raw data values, instead, it has data that has been pre-processed. The dataset contains data representing the college majors of American citizens and how earning is distributed among the fields of study. Secondary data was preferred to save on time and extensive costs that would have been incurred when collecting primary data. Similarly, secondary data has been pre-cleaned and stored in the format that is easy to use for academic work. Further, the data could not have been collected easily considering that it consists information on finances which can prove to be a sensitive matter among different people.

Data Analysis

Data analysis was performed in the STATA software statistical package. The software is widely used to produce quality and integrated statistical outputs including easy to understand graphics. The main data analysis procedure is the use of descriptive statistics to understand the nature of the data and the relationship between the variables. Correlation and simple linear regression analysis are also carried out.

Data is first visualized through the use of a histogram and scatter plot to display the underlying patterns in the dataset. Data is then grouped to understand the college majors receiving high pay. A regression model is developed to predict the mid-year career salaries. Below is the theoretical model.

FINDINGS

The graph below represents the distribution of the starting median salaries across undergraduate degrees.

From the graph, most of the college majors have a starting median salary of less than 40000 US dollars. On the other hand, the fewest number of undergraduate degrees have a return of more than 60000 US dollars. The distribution of the starting median salaries across the college majors seem to possess a skewed pattern. There is no normal distribution of the starting salaries, implying that few fields of study pay very high at entry-level while the majority of the undergraduate degrees pay a relatively low amount of salaries. The histogram below depicts the progression to mid-career level in terms of salaries.

The histogram above represents data on the percentage change from the starting to mid-career salary among the college major data points. The graph shows a normal distribution implying that the percentage change among the college majors is normally distributed. The figure shows that the change in salaries from the starting to the mid-career salaries peaks at 70 percent, implying that individuals in many college majors usually receive a 70 percent increase in their salaries from their entry level wages. However, a significant number of college majors witness a 100 percent change from the starting salary at the mid-career level. On the other hand, the lowest number of college majors have a 40 percent increase in their salaries from the entry level to the mid-career level. Below is a table showing descriptive statistics of the starting salaries.

The above table shows that from the 50 observations representing different college majors, the mean of the starting median salaries is 44310. The starting salaries vary from each other among the undergraduate degrees by 9360.366. The lowest and highest values of the starting median returns was 34000 and 74300 US dollars respectively. An illustration of the summary statistics on the mid-career salaries is given below.

The table above shows that the 50 undergraduate degree observations payoff a mean of 74786 US dollars at the mid-career level of work. The standard deviation among the mid-career level salaries is 16088.4. Elsewhere, the lowest median earnings among the college majors at mid-career level is 52000 US dollars, while the highest returns among for the fields of study at mid-career is 107000 US dollars. Data from the degrees pay-back dataset were regrouped into specific combinations of academic majors to understand the level of median salaries earned in the categories. A random sample of six undergraduate degrees is made from the 51 observations to form each three categories.

The table above has data representing the engineering major. From the table, the lowest engineering field in terms of returns is Architecture with the median starting salary of 41600, while the major with the highest starting salary is chemical engineering with returns of 63200 US dollars. The highest earning major at the mid-career at the 75^th percentile is Chemical Engineering with 143000 US dollars. The second category is the education and science category.

The table above represents category including the fields of education and sciences. Other majors in the group are Math, and Geography. It is observed that the starting median salaries in the category range from 34900 US dollars to 50300 US dollars. Education major has the lowest starting median salary, whereas Physics has the highest. On the other hand, the Mid-career 75^th percentile salaries among the college majors range from 73400 US dollars to 128000 US dollars. Mathematics field has the highest mid-career 75^th percentile salary of 128000 US dollars. The third category of college majors comprise of Business, Information technology and arts.

From the table above, the starting median salaries in the category range from 35600 to 49100 US dollars. The lowest earning college major at entry-level from the category is journalism, while the highest earning college major at entry-level is Information Technology. On the other hand, the mid-career 75^th percentile salaries in the category range from 81200 US dollars to 119000 US dollars. Sociology has the lowest earnings at mid-career 75^th percentile, while Marketing the highest earning at mid-career 75^th percentile with 119000 US dollars.

Correlation

Below is a table representing the correlation of the starting salaries and the mid-career salaries amongst the college majors.

The pearson’s correlation coefficient between the two variables is 0.8485, implying that the starting median salary and the mid-career median salary variables are strongly positively correlated. Therefore, a linear regression analysis can predict the mid-career salaries of employees in the different college majors. The scatter plot below represents the relationship between the starting median salary and the mid-career median salary. The graph explains the how gaining of experience affects salaries across different college majors.

From the graph, there is a linear relationship observed between the starting median salaries and the mid-career median salaries. The graph gives evidence that undergraduate degrees with high starting median salaries are likely to have high mid-career wages. A regression analysis is carried out to predict the mid-career median salaries given the starting median wages. Below are the regression statistics.

Regression Analysis

From the above table, the p-value is less than the significant level of 0.1, implying that the regression model is statistically significant. The value of R squared is 0.7199, illustrating that 71.99 percent of the variation in the mid-career median salaries among the college majors is caused by the starting median salaries. The implication is that the starting median salaries among the fields of study significantly predicts the mid-career salaries that the college majors pay off.

The table above illustrates the analysis of variance results obtained from the regression analysis. The sum of squares of the model is 9.1302e+09 with 1 degree of freedom, while the total sum of squares is 1.2683e+10 with the 49 degrees of freedom. The mean sum of squares of the model, the residuals and the total mean sum of squares are presented in the table. The table below illustrates the coefficients from the regression analysis.

From the above table, the coefficient representing the constant of the model is 9.1302e+09. The coefficient is statistically significant since the t-test p-value is below the selected significance level of 0.1. The coefficient representing the starting median salary is 3.5528e+09 with a t-test p-value of 0.093. The t-test p-value is less than the selected significance level of 0.1, implying that the coefficient is statistically significant. The model resulting from the regression analysis is obtained to be as follows.

From the model, an addition of the constant value and  and  will predict the mid-career salary at 90 percent confidence.

DISCUSSION AND CONCLUSIONS

The purpose of the present study was to examine the variation of salaries according to college majors. It sought to prove the traditional theory that engineering majors have high returns than other majors of education such as education, arts, and humanities. This study went further to estimate the influence of experience of individuals in different fields of study on the wages received by comparing the starting median salaries and the mid-career wages. This study used data that has been pre-processed for the analysis. This section discusses the findings of the study.

The preliminary analysis on the data employed data visualization technique through the graphics command in STATA. A histogram showed that most of the college majors pay off salaries between 30000US dollars and 40000 US dollars at entry-level. The lowest frequency of the undergraduate degrees pay over 60000US dollars as the starting salary. The second histogram displayed the distribution pattern of the percentage change of salaries from the entry-level to the mid-career level. The graph showed a normal distribution across the college majors, implying that majority of the college majors have mid-career salaries proportional to the starting salaries. This was highly expected since an individual is expected to have a rise in returns at the mid-career (Altonji and Pierret , 2001).

This study went ahead to examine summary statistics in the starting median salary and the mid-career median salary variables. From the statistics, the mean amount of salaries paid to individuals across different fields of study is 44310 US dollars, whereas the average amount of pay people in various college majors receive at mid-career level is 74786. Therefore, there is evidence that the more time one stays at work in a particular field of study increases the amount of salary one gets. Besides, there is a large gap between the salaries received at mid-career level and the entry-level. It can be concluded that experience is a determinant in the amount of salaries people working in different college majors receive.

The different fields of study in the dataset were then regrouped into categories and six undergraduate degrees randomly selected to represent each category. The categories included fields in the engineering sector, education and science industry, and arts, languages, information, and humanities. In the engineering group, the salaries between the starting median salaries and the mid-career 75^th percentile salaries was 41600 and 143000. Individuals in the chemical engineering field earn the highest at entry level and the mid-career level. On the other hand, people in the architecture sphere earn less than other engineering majors at both entry and mid-career 75^th percentile level. However, from the analysis, engineering fields have a small variance of salaries at both the entry and the mid-career level.

The second category consisted of majors in education, sciences, history, and geography. The range between the entry-level median salaries between the sampled majors in the category was from 34900US dollars to 50300 US dollars. At the mid-career level, the highest earning undergraduate degree was physics at 114000 US dollars. Similar to the engineering sphere, there are small differences between salaries at the entry level and mid-career level between the college majors. The majority of the undergraduate degrees in the category earn less than the corresponding engineering fields both at the starting median salaries and the mid-career salaries. However, individuals in the Physics college major have a median salary return that is close to many engineering fields.

The third category of the undergraduate degrees consisted of fields in business, arts, humanities, languages, and information technology spheres. The highest earning majors from the category at entry-level were Business Management and Information technology. The range between the lowest starting median salaries and the highest mid-career median salaries was from 35600 US dollars to 119000 US dollars. The salaries in the starting and mid-career levels among the fields of study in the category are less when compared to the engineering fields. From observational analysis, it can be concluded that individuals in engineering fields are likely to earn more both at entry-level and at the mid-career level than people in other fields of study. This study’s results concur with the evidence provided by research studies by Van de Werfhost ( 2002), Altonji et al. (2012), and Ransom (2014) that different fields of study attract different pays.

The present study went ahead to measure the impact of experience of individuals in different college majors on the amount of salaries received. This was done by comparing the starting median salary variable and the mid-career median salary variable through correlation and regression analysis. It was revealed that the starting salaries are highly correlated to the mid-career salaries paid to employees in varying fields of study. A scatter plot showed similar results of a linear relationship between returns at the entry-level and the mid-career level. A regression analysis was carried out to ascertain whether the mid-career salaries can be predicted by the starting salaries among different college majors. A regression model obtained was found to be statistically significant and the variation in the mid-career salaries can be significantly explained by the model. Therefore, it can be concluded that experience affects the amount of salaries received in different college degrees. Similarly, when comparing the categories of different majors, the salaries at mid-career significantly varied from the starting salary. In line with evidence by Altonji and Pierret (2001), individuals in some majors may only earn more than those in other majors due to differences in experience.

Recent times have seen an increase in students enrolling in higher learning institutions. There is need to have studies to enable people make the right choices on which courses to study in relation to the amount of returns received. Several scholars prove that there is a strong relationship between the amount of payoffs and the college major studied. This study sought to prove the social theory that engineering majors earn more than other fields of study such as education, humanities, arts, and sciences. The findings of this study indicate that indeed individuals who study engineering majors are likely to earn more than those in other fields. The call by governments to have reforms in the education sector to have more entrants in the STEM courses is, therefore, supported by this study.

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