Caydee Blankenship, Bashir Khan, Corey Okun, and Garett Waters
Continuing your education is important for many reasons such as having job security, improving your mental well-being, or increasing your wages. Overall, this chapter will discuss the returns and benefits of obtaining more education. First, the reasons why economists believe that schooling is beneficial and therefore pay off for students will be discussed. Additionally, even though education creates many private benefits for the individual, there are many benefits for society as a whole when more people are educated. These social benefits will be analyzed and will explain why society is better when more people receive an education. Finally, data from the American Community Survey (2019) is used to estimate an earnings equation to examine the wage differentials across individuals and the return to alternative college majors.
Why Schooling Pays Off
Investments into education can provide benefits to income, increased job satisfaction, and personal and family health. These benefits can persist and manifest even in the next generation, with better results even being seen in the children of those who invest in higher education. These benefits can be a direct result of education, or a result of a result of higher education such as income advantages.
Higher education has a well-established benefit on wages, wage growth, and financial stability overall. For recent college graduates, wages are similar, yet slightly higher in most cases to people of the same age who did not seek additional schooling outside of high school (Houthakker, H. S.1959). However, a gap forms and becomes wider as these two groups continue working throughout their lifetime with wages for both groups peaking around age forty-five. This wage benefit is supported by both the human capital model and screening theory in terms of how higher education can affect wages and worker prosperity in the labor market. According to the human capital model, as workers gain more human capital and skills through education, they become more valuable to employers, and can negotiate for better wages. According to the screening theory, achieving higher education signals to employers that the worker possesses skills greater than those who did not achieve higher education, giving the worker more perceived value to employers. Through both of these theories, an individual who pursues higher education is more attractive to an employer, than a worker who only gets the basic primary education and directly results in a higher wage premium. This improvement to financial stability from higher education comes with several secondary improvements to the quality of life such as health and mental well-being (Ogden, C. L, 2018). The benefits to wages that come with higher education are well understood from a theoretical perspective, and additionally born out historically to cement this concept.
One noticeable way health can relate to education and wages is through obesity. Obesity is an ever-growing problem in developed nations, and a condition that disproportionately affects those in poverty (Ogden, et al, 2018). In a study done using data collected from the National Health and Examination Survey in the early 2000s to the mid-2010s by Ogden (2018), rates of childhood obesity were grouped based on household income and educational attainment. For households headed by someone who are college graduates, there is a difference of as much as ten percentage points less likely to be obese when compared to those did not attend college. Households with some college fared better than those with no college, and the gap in childhood obesity is wider for females than it is for males (Ogden, et al, 2018). These health differences seen between households of different education are an example of how the benefits of education can persist to the next generation.
Mental health is also heavily affected by the wages an individual takes home. In a large survey by Cacciatore, Killian, and Harper (2016), that recruited from a long established online parental bereavement support forum, persons who often visited the website were asked to fill out a questionnaire on post-traumatic symptom prevalence following the death of a child. Factors considered included income and education levels of the parents, time passed since the death of the child, age of the child at the time of death, and how the child died. What the study found was that the strongest indicator for significant levels of post-bereavement stress was income at the time of grieving (Cacciatore et al, 2016). Mothers in the lowest socioeconomic levels with the lowest education levels fare the worst in terms of post-bereavement stress. A noticeable difference is seen in the number of accessible mental health resources available to those with lower socioeconomic status, especially when compared to mothers with high socioeconomic status and higher education. What can be taken from this is that barriers between bereaved parents and support resources are based on income, and that those with higher incomes and education levels have an easier time accessing those resources. These increases in wages as well as benefits of those higher wages make a compelling case in favor of pursuing higher education if the option exists.
The benefits of higher education can manifest in ways outside of just how much money they take home, but in job satisfaction. In a study using data from a survey obtained from The European Community Household Panel, workers were asked about their job satisfaction. Respondents were between ages 24-26, separated by gender and amount of schooling completed, and actively worked a minimum of fifteen hours per week (Fabra, M. E. 2009). Job satisfaction can be broken down into several specific categories: Wage satisfaction, work environment, commute time, job stability, and coworker satisfaction. Respondents were asked to report their satisfaction on a scale of 1-10 based on these categories. Higher education has been shown to have a positive effect on overall job satisfaction (Fabra, M. E. 2009). While wages play a part in satisfaction, people with higher education seem to report higher job satisfaction in many other less direct aspects, such as work environment and job fulfillment.
Overall, there are noticeable outcome-related differences between those who choose to pursue and complete higher education and those who do not. These differences can be financially related to their job, and secondary effects felt outside the job in everyday life. These outcomes can come in the form of better income for those with higher education, which is a benefit that brings its own set of benefits such as better access to various mental and nutritional health resources. These outcomes on nutritional health have a direct impact on the next generation. Education itself also seems to have an impact on whether or not an employee is happier with their profession. All of these benefits should make it plain to see why higher education pays off for those who achieve it.
Social Versus Private Returns of Going to School
Education has many types of returns over time, and education simultaneously has private and social returns. Many studies have addressed these returns. Private returns are individual returns, which can be earnings, health, and many more. The social return is a benefit to the whole society, which isn’t merely captured by the recipient of education. A study done by Erik Clanton (2007) examines returns to education where it predicts that an increase in one year of education increases labor productivity by 7-10% in the short-run and 11-15% in the long run. Education has many other aspects of social and private returns. The reduction in the crime rate is one of the most significant aspects addressed by Lochner and Moretti (2004). The author examines the effect of education on the crime rate and found out that “crime reduction associated with high school graduation is about 14-26 % of the private return” (Lochner et al., 2004). It is hard to estimate the direct return of education on crime due to many other variables which are hard to measure, such as personality traits. Education may also change the personality traits which are associated with crime. Lochner (2004) suggests that education may reduce the crime rate by increasing one’s patience or risk aversion.
Another significant social return of education is generally improved public health. It seems that countries with higher educational attainment usually have higher life expectancies. Education can possibly change decision-making patterns regarding health and instigate awareness about the significance of preventive care. The improved literacy may help understand the intricate issues essential to an individual’s well-being. David and Adriana (2006) examine the importance of education on well-being by showing that an increase in four years of education lowers five-year mortality by 1.8 percentage points. It also reduces the risk of heart disease by 2.16 percentage points and further reduces the risk of diabetes by 1.3 percentage points. The experiment demonstrates that education dramatically improves lifestyle choices, improving the overall quality of life. Richard et al. (2005) measure the effect of education on health, and they found that people with more education are less likely to smoke or involve in binge drinking. Simultaneously, they are more likely to exercise frequently and have healthier diets. The same experiment also addresses some significance of education on women’s health choices. It shows that U.S women enrolled in college for two years are less likely to smoke during pregnancy.
The increase in educational attainment reduces the chances of unemployment, increases government tax revenue, and reduces the chances of dependence on public assistance programs. Trostel et al. (2015) show that people with higher education pay more taxes over a lifetime period. The paper shows that a high school graduate pays 137 thousand dollars in taxes over a lifetime. The lifetime taxes are 50 thousand dollars greater for a college drop-out, 65 thousand dollars greater for associate degrees, and 192 thousand greater for college degree receivers. It can be understood by the fact that higher educational attaintment usually results in higher-earning jobs, which means higher taxes. Trostel et al.(2015) further mention that the likelihood of someone being unemployed with a bachelor’s degree is 2.2 times lower than someone with a high school degree.
The increased educational attainment strengthens social engagement in many aspects, and education also increases the probability of civic and political engagement. File et al. (2012) found a correlation between educational attainment and voter participation. It shows that the voting rate in the 2008 U.S election was 79 percent higher for someone with a bachelor’s degree compared to high school dropouts. Moreover, educational attainment’s effect on community engagement is shown in a research paper by Trostel et al. (2015). By comparing the recipients of bachelor’s degrees in 2012 with high school graduates, it mentions, “Participation in school, community, service, civic and religious organizations is substantially (1.9 times) higher. Leadership in these organizations is particularly (3.2 times) greater.” (Trostel et al., 2015)
Successful marriages are essential for the growth of future generations, and educational attainment has been shown to correlate with educational attainment positively. Trostel et al. (2015) calculated the marital status correlation with educational attainment from the 2012 American Community Survey. It is found that the married percentage increases with educational attainment; for instance, the married people percentage rises from 55 percent for high school graduates to 67 percent for someone with a bachelor’s degree. Moreover, in the same experiment, a substantial difference in divorce rate is found with having a bachelor’s degree compared to an associate degree. According to the study, 18.6 percent of people are divorced with associate degrees, whereas 12.4 percent of divorced people have bachelor’s degrees.
Some studies have also mentioned the happiness correlation with educational attainment. An experiment run by Oreopoulos and Salvanes (2011) shows that educational attainment positively correlates with happiness or overall life satisfaction. The experiment shows that 89 percent of people with a high school degree are happy. A person who attended but has not fully completed college has 90 percent of people reported being happy. For those with bachelor’s degrees, the percentage rises to 94 percent. However, the happiness index is also connected with other variables, such as earnings, so the direct correlation between educational attainment and earnings is not that strong. (Trostel et al., 2015)
The impact of the personal return of education is very significant. Studies have shown that education dramatically impacts an individual’s earnings and wealth. Education explicitly opens doors to many opportunities for an individual and reduces the chance of unemployment. A Consensus Bureau study was done in 2002 estimated that in 1999, the average lifetime earnings of a bachelor’s degree was 75% higher than high school graduates ( Carnevale et al., 2013)
Education has significantly improved people’s financial literacy, and people get equipped with knowledge and awareness through which they can make better financial decisions. Trostel et al. (2015) compared the American receiving a bachelor’s degree in 2012 with high school graduates, and they found that having a bachelor’s decreases the incidence of poverty by 3.5 times. Furthermore, the asset income is 4.9 times higher than high school graduates, and the dependency on expensive credit sources is also lower. The asset income of graduate students is even higher, and it is 7.6 times greater than high school students. Overall, it shows that education increases financial acumen.
The increase in earnings or financial acumen is not the only return from education. Trostel et al. (2015) mention the average annual employer contributions for health insurance. The value of employer contribution towards employee health insurance is significantly higher for higher educational attainment. A person with an associate’s degree receives 1,859 dollars, whereas a bachelor’s degree receives 3,226 dollars on average in health insurance. The contribution to the retirement plan from the employer is another basic form of compensation. Although no data shows the details about retirement benefits, people with higher educational attainment generally have higher retirement compensations. Trostel et al. (2015) mention the increase in retirement income with higher educational attainment. A person with a bachelor’s degree receives 10,721 dollars in retirement income, whereas a high school graduate receives 4,458 dollars in retirement income. Job prestige is another substantial return from higher educational attainment. Although every job plays a significant role in society, jobs with greater prestige generally require higher degrees. Oreopoulos and Salvanes (2011) explore the relationship between occupational prestige and educational attainment. The prestige in the experiment is based on subjective rankings from respondents—the scores range from 17 to 86. The average score for high school graduates is 38, whereas the average for a bachelor’s degree is about 53.
The intergenerational benefit is another significant benefit of higher educational attainment, and it ensures the prosperity of future generations, which is essential to human development. Hann (2011) used Wisconsin Longitudinal Study to find the effect of parents’ education on children’s educational attainment. The study derived the results from 21,545 observations, showing some drastic benefits. The children of a mother who has a bachelor’s degree as her highest qualification averaged 1.95 more years of education than mothers with high school graduates. Similar results can be derived from fathers’ education. The study shows that children with a father with a bachelor’s degree add 1.7 years of children’s education on average compared to fathers with high school diplomas. Although these numbers are not that huge, they can significantly impact children’s future income, health, wealth, and marriage partners.
Higher educational attainment appears to make people more generous towards society. Trostel et al. (2015) mention philanthropy in different aspects, such as volunteering, cash donation to charity, and employment in non-profit organizations. “The volunteer proportion increases from just over 17 percent for high school graduates with no college to 28 percent for those with some college but without degrees, to 40 percent for those with bachelor’s degrees but without advanced degrees, to nearly 49 percent for those with graduate degrees.” ( Trostel et al., 2015) The increase in volunteering is quite substantial. Philanthropy by obtaining a job in a non-profit organization is critical, and the individual is putting in his time and effort towards a good objective by accepting lower salaries. Trostel et al. (2015) show that people with higher educational attainment are the ones who usually work in a non-profit organization. The data is captured using employment and earnings data in the 2012 American community survey. It provides significant results showing that around 15 percent of the people with graduate degrees work in a non-profit organization, whereas 5 percent of employed high school graduates work in a non-profit organization. Another essential thing to be taken into account is that the average earnings of non-profit workers were 2.1 percent less than those who work in profit-oriented organizations.
The increase in educational attainment also increases the probability of charitable contributions. Using a consumer expenditure survey (2012), Trostel et al. (2015) show that annual cash contributions towards charity are high among people with higher educational attainment. “Average total annual cash contributions rise from less than $400 (1.6 percent of their average earnings) for high school diploma and no college to about $600 (2.0 percent of their earnings) for those some college but no degree, to $700 (1.9 percent) for associate degrees, to $1,300 (2.3 percent) for bachelor’s degrees but no advanced degree, to nearly $2,400 (2.9 percent) for those with advanced degrees.” Trostel et al. (2015) The study shows that cash contribution increases toward religious, charity, and educational contributions. However, a minor increase was seen in cash contribution towards religious institutions with the increase in educational attainment.
Higher educational attainment decreases the chances of living on public assistance. Trostel et al. (2015) used data from six types of social assistance taken from the March Social and Economic Supplement of Current Population Survey. The six types include the insurance value of Medicaid, Supplemental Nutritional Assistance Program (SNAP), school lunches for children, housing subsidies, cash assistance, and energy assistance. The results showed that associate graduates receive 41 percent less public assistance than high school graduates. Simultaneously, people with bachelor’s degrees receive 73 percent less public assistance than high school graduates. The decrease in reliance on public assistance increases the total savings for taxpayers. Trostel et al. (2015) further mention that the savings to taxpayers from reduced total public assistance is about $22,000 for associate degrees and $40,000 for bachelor’s degrees.
Moreover, the likelihood of living in poverty decreases with increased educational attainment. Trostel et al. (2015) used the U.S. average poverty thresholds in 2012 and found that a bachelor’s degree holder is 3.5 times less likely to be living with income under the poverty line. The results also showed that 1 out of every 6.5 high school graduates lives in poverty. In contrast, poverty decreases by 27 percent for even a college dropout compared to a high school graduate. Overall, increased educational attainment brings broad returns in different domains of life, and both social and private returns play a massive role in the prosperity of humanity.
Earnings Analysis
Clearly, there are significant benefits of obtaining more years of schooling. These benefits can be private, but can also improve society as a whole. Nonetheless, an important reason why many people continue their education is to increase their potential earnings. This section of the chapter examines the determinants of wage differentials across individuals. These variables include gender, age, educational attainment, nativity, race, usual hours worked per week, marital status, and place of work for an individual. After seeing which variables are significant in determining an individual’s wages, an OLS regression will be done to assess if certain majors significantly increase earnings among college graduates who have obtained a bachelor’s degree.
Previous work done by James (2012) and Hout (2012) has shown that there is a significant return to higher levels of educational attainment. For example, there are significant benefits of obtaining a bachelor’s degree, no matter the major choice, compared to getting a high school diploma. However, those who obtain an advanced degree (6 or more years of college, a master’s degree, professional degree, or PhD), experienced a 20% wage premium in the 1980s and a 30% wage premium, now (James, 2012). Due to this incentive Americans have been going to school for longer periods while striving to obtain higher levels of education. Also, according to Hout (2012), earnings in relation to gender, education, and race have all been positively sloping. Individuals that pursue this long-term educational commitment not only make more money but have been shown to live happier lives as well. By offering and expanding educational opportunities the U.S. has seen appreciable gains.
Even though there are significant benefits to obtaining any degree, there has also been research done that supports the concept that different majors earn higher wage premiums after graduation. Especially during times of recessions and high unemployment, students are more likely to choose majors that are known for earning higher wages. For example, both males and females were more likely to choose more difficult majors (STEM) during recessions because they believe they will be able to earn more money after graduation (Blom et al., 2021). The study done by James (2012) stated that engineering majors had the highest wage premium which amounted to 125% and social work majors had the lowest premium, 40%. Furthermore, research conducted by Rossi and Hersch (2007) has found that having a double major significantly adds a 2.3% increase to the earnings of individuals whose highest level of education is a bachelor’s degree. Students that study quantitative or technical fields have shown to have the highest marginal returns. Such STEM students who double in two STEM majors obtain these larger gains. However, if a student majors in engineering while also majoring in liberal arts the same positive correlation does not occur. Most gains are generated from choosing two majors that have similar characteristics to one another.
Finally, previous research concludes that people from different demographics receive a higher return to college compared to others. Cooper and Cohn’s (1997) study focuses on the returns of higher education by race and gender, by considering all educational benefits (and costs), which include both monetary and nonmonetary components. Their results found that women, especially black females, tend to have a larger return when investing in their education, compared to males.
Data
The data was collected from the 2019 American Community Survey (ACS). The sample consists of those individuals who are between the ages of 35 and 55. Also, the individuals have to be considered “full-time workers,” meaning they worked more than 32 hours per week on average. Table 1 includes all of the variables used in the models and their descriptions.
Table 1: Variables and Their Descriptions
| Variable | Description |
| l_wagep | = log of wages or salary income for the past 12 months for individual i. |
| agep | = age of individual i. |
| sq_agep | = age of individual i squared. |
| schl | = educational attainment for individual i. |
| lhs | = dummy variable for less than a high school diploma. |
| hs | = dummy variable for a high school diploma. |
| ged | = dummy variable for a GED or alternative credential. |
| somecollege | = dummy variable for some college but no degree. |
| associate | = dummy variable for an associate’s degree. |
| bachelor | = dummy variable for a bachelor’s degree. |
| master | = dummy variable for a master’s degree. |
| professional | = dummy variable for a professional degree beyond a bachelor’s degree. |
| doctorate | = dummy variable for a doctorate. |
| sex | = sex of individual i. |
| female | =dummy variable for females. |
| male | =dummy variable for males. |
| nativity | = nativity for individual i. |
| foreign | = dummy variable for foreign-born. |
| native | = dummy variable for natives. |
| rac1p | = race code for individual i. |
| white | = dummy variable for White. |
| black | = dummy variable for Black. |
| americanindian | =dummy variable for American Indian. |
| alaskanative | =dummy variable for Alaska Native. |
| indianalaska | =dummy variable for American Indian and Alaska Native tribes specified or American Indian or Alaska Native not specified. |
| asian | = dummy variable for Asian. |
| pacificislander | = dummy variable for Native Hawaiian and other Pacific Islander. |
| otherrace | = dummy variable for some other race |
| mixedrace | = dummy variable for two or more races. |
| wkhp | = usual hours individual i worked per week for the past 12 months. |
| married | = dummy variable for married. |
| widow | = dummy variable for widowed. |
| divorce | = dummy variable for divorced. |
| separated | = dummy variable for separated. |
| single | = dummy variable for single. |
| powsp | = place of work for individual i. |
| northeast | = dummy variable for Northeast (ME, NH, VT, MA, CT, RI, NY, PA, or NJ). |
| midwest | = dummy variable for Midwest (OH, IN, IL, MI, WI, MN, IA, MO, KS, NE, SD, or ND). |
| west | =dummy variable for West (WA, OR, CA, NV, ID, MT, WY, UT, CO, AZ, or NM). |
| south | =dummy variable for South (TX, OK, AR, LA, MS, AL, FL, GA, SC, NC, TN, KY, WV, VA, DC, MD, or DE). |
| pacific | =dummy variable for Pacific (AK or HI). |
| otherregion | =dummy variable for other regions (Puerto Rico, Europe, Eastern Asia, other Asia not specified, Canada, Mexico, Americas not specified, or other US Island areas not specified, Africa, Oceania, at sea, or abroad). |
| fod1p | = field of degree for individual i. |
| accounting | = dummy variable for Accounting fields. |
| actuarial | = dummy variable for Actuarial Science fields. |
| agriculture | = dummy variable for Agriculture fields. |
| architecture | = dummy variables for Architecture fields. |
| biology | = dummy variable for Biology fields. |
| business | = dummy variable for Business fields, not Finance |
| chemistrypremed | = dummy variable for Chemistry and Pre-Med fields. |
| communications | = dummy variable for Communication fields. |
| computer | = dummy variable for Computer-Related fields. |
| earlyeducation | = dummy variable for Early and Elementary Education fields. |
| economics | = dummy variable for Economics fields. |
| othereducation | = dummy variable for other Education fields. |
| engineering | = dummy variable for Engineering fields |
| environmental | = dummy variable for Environmental and Natural Resource fields. |
| familyconsumer | = dummy variable for Family and Consumer Sciences fields. |
| finance | = dummy variable for Finance fields. |
| industrial | = dummy variable for Industrial and Commercial Arts fields. |
| journalism | = dummy variable for Journalism fields. |
| leisure | = dummy variable for Leisure Study fields. |
| liberalarts | = dummy variable for Liberal Arts and History fields. |
| language | = dummy variable for Literature and Languages fields. |
| math | = dummy variable for Mathematics and Statistics fields. |
| nursing | =dummy variable for Nursing fields. |
| othernaturalscience | =dummy variable for other Natural Science fields. |
| otherfields | =dummy variable for other fields. |
| politicalscience | =dummy variable for Political Science and International Relations field |
| pharmacy | =dummy variable for Pharmacy fields. |
| physics | =dummy variable for Physics fields. |
| prelawlegal | =dummy variable for Pre-Law and Legal Study fields. |
| protective | =dummy variable for Protective Service fields. |
| psychology | =dummy variable for Psychology fields. |
| publicaffairs | =dummy variable for Public Affairs, Health, and Policy fields. |
| othersocialscience | =dummy variable for other Social Science fields. |
| socialwork | =dummy variable for Social Work fields. |
| sociology | =dummy variable for Sociology fields. |
| technicalengineering | =dummy variable for Technical Engineering fields. |
| technicalhealth | =dummy variable for Technical Health fields. |
| visualarts | =dummy variable for Visual and Performing Arts fields. |
Table 2: Consistent Field of Study
| Variable | Consistent Field of Study | ACS Components |
| accounting | Accounting | Accounting |
| actuarial | Actuarial Science | Actuarial Science |
| agriculture | Agriculture | General Agriculture, Agriculture Production and Management, Agricultural Economics, Animal Sciences, Food Science, Plant Science and Agronomy, Soil Science, and Miscellaneous Agriculture |
| architecture | Architecture | Architecture |
| biology | Biology Fields | Botany, Zoology, Ecology, Pharmacology, Miscellaneous Biology, Biology, Molecular Biology, Genetics, Microbiology, Physiology, Cognitive Science and Biopsychology, and Neuroscience |
| business | Business Fields, not Finance | Management Information Systems and Statistics, Business Management and Administration, Marketing and Marketing Research, Miscellaneous Business and Medical Administration, General Business, Operations Logistics and E-‐Commerce, Business Economics, Human Resources and Personnel Management, International Business, and HospitalityManagement |
| chemistrypremed | Chemistry and Pre-Med | Biochemical Sciences, Chemistry, and Health and Medical Preparatory Programs |
| communications | Communication Fields | Communications, Communication Technologies, Mass Media, and Advertising and Public Relations |
| computer | Computer-Related Fields | Computer Programming and Data Processing, Computer and Information Systems, Computer Science, Information Sciences, Computer Information Management and Security, and Computer Networking and Telecommunications |
| earlyeducation | Early and Elementary Education | Elementary Education and Early Childhood Education |
| economics | Economics | Economics |
| othereducation | Other Education Fields | Physical and Health Education Teaching, Secondary Teacher Education, Special Needs Education, Teaching Education: Multiple Levels, Language and Drama Education, General Education, Educational Administration and Supervision, School Student Counseling, Mathematics Teaching Education, Science and Computer Teacher Education, Social Science or History Teaching Education, Art and Music Education, Miscellaneous Education, and Library Science |
| engineering | Engineering Fields | Chemical Engineering, Civil Engineering, Electrical Engineering, Mechanical Engineering, General Engineering, Aerospace Engineering, Biological Engineering, Architectural Engineering, Computer Engineering, Engineering Mechanics, Physics, and Science, Environmental Engineering, Geological and Geophysical Engineering, Industrial and Manufacturing Engineering, Materials Engineering and Material Science, Metallurgical Engineering, Mining and Mineral Engineering, Naval Architecture and Marine Engineering, Nuclear Engineering, Petroleum Engineering, Miscellaneous Engineering, and Biomedical Engineering |
| environmental | Environmental and Natural Resource Fields | Forestry, Environment and Natural Resources, Environmental Science, and Natural Resource Management |
| familyconsumer | Family and Conumer Sciences | Family and Comsumer Sciences |
| finance | Finance | Finance |
| industrial | Industrial and Commercial Arts | Precision Production and Industial Arts and Commercial Art and Graphic Design |
| journalism | Journalism | Journalism |
| leisure | Leisure Studies | Physical Fitness, Parks, Recreation, and Leisure |
| liberalarts | Liberal Arts and History Fields | History, Liberal Arts and Humanities, Liberal Artsm Humanities, Philosophy and Religious Studies, Theology and Religious Vocations, and United States History |
| language | Literature and Languages Fields | French, German, Latin, and Other Common Foreign Language Studies, Other Foreign Languages, Linguistics and Foreign Languages, Linguistics and Comparariive Language and Literature, English Language, Literature, and Composition, English Language and Literature, and Composition and Speech |
| math | Mathematics and Statistics | Mathematics, Statistics and Decision Science, Applied Mathematics, Mathematics and Computer Science |
| nursing | Nursing | Nursing |
| othernaturalscience | Natural Science Fields, Other | Geology and Earth Science, Physical Sciences, Atmospheric Science and Meteorology, Geosciences, Oceanography, and Multi-disciplinary or General Science |
| otherfields | Other Fields | Military Technologies, Interdisciplinary and Multi-Disciplinary Studies, Transportation Sciences and Technologies, Cosmetology Services and Culinary Arts, Construction Services, and Electrical and Mechanic Repairs and Technologies |
| politicalscience | Political Scinece and International Relations | Political Science and Government and International Relations |
| pharmacy | Pharmacy | Pharmacy, Pharmaceutical Sciences, and Administration |
| physics | Physics | Physics and Astronomy and Astrophysics |
| prelawlegal | Pre-Law and Legal Studies | Pre-Law and Legal Studies and Court Reporting |
| protective | Protective Services | Criminal Justice and Fire Protection |
| psychology | Psychology Fields | Psycholgy, Educational Psychology, Clinical Psychology, Counseling Psychology, Industrial and Organizational Psychology, Social Psychology, and Miscellaneous Psycholgy |
| publicaffairs | Public Affairs, Health, Policy | Public Adminstration, Public Policy, and Community and Public Health |
| othersocialscience | Social Science Fields, Other | Area, Ethnic, and Civilization Studies, Anthropology and Archeology, Geography, Intercultural and International Studies, Interdisciplinary Social Sciences, General Social Sciences, Criminology, and Miscellaneous Social Science |
| socialwork | Social Work | Social Work and Human Services and Community Organization |
| sociology | Sociology | Sociology |
| technicalengineering | Technical Engineering Fields | Engineering Technologies, Engineering and Industrial Management, Electrical Engineering Technology, Industrial Production Technologies, Mechanical Engineering Related Technologies, and Miscellaneous Engineering Technologies |
| technicalhealth | Technical Health Fields | Nutrition Sciences, Medical Technologies Technicians, Medical Assisting Services, Nuclear, Industrial Radiology, and Biological Technologies, General Medical and Health Services, Health and Medical Administrative Services, Miscellaneous Health Medical Professions, Communication Disorders Sciences and Services, and Treatment Therapy Professions |
| visualarts | Visual and Performing Arts | Art History and Critism, Fine Arts, Music, Drama and Theater Arts, Film, Video and Photographic Arts, Miscellaneous Fine Arts Studio Arts, and Visual and Performing Arts |
Dependent Variable
This study focuses on analyzing an individual’s wages. Therefore, the dependent variable that was used during the research in this study is the natural log of wages. To properly account for changes in wages among individuals, this study observes the wages that the individual accrued in the last 12 months. Additionally, the log of wages was used because wages typically increase by percentages rather than dollar amounts. For example, wages are more likely to increase by 3% instead of by $3.00.
Independent Variables
For this study independent variables were chosen to measure different sociodemographics for those who pursue higher education, followed by the future outcome and earnings of that degree. This allows for the results of the study to include a wide and diverse variety of the different returns on education. The study’s sample size consists of subjects who are 35-55 years old. This is typically when the average working individual is known to be accumulating their highest salary; also known as the earnings peak. However, age is still included in the regression because as someone gets older, it is expected that their earnings increase. This is due to raises and because the individual will have more knowledge and experience in the job that they are in. As a result, the employer would increase their wages because they are more productive. It is also known that wages increase at a decreasing rate as employees get older. Therefore, the squared value of age is also included in the regression.
The educational attainment of the individual was included in the model when estimating the wages because there is a large gap between those who get an additional year of schooling or another degree compared to those who fail to complete high school. For instance, students with no high school education are known to be the lowest-earning workers in the U.S. When obtaining a high school diploma or GED earnings, these earnings are still very low when compared to students that continue their education into college. Overall, as more schooling is completed one’s earnings continue to increase. As people complete more schooling, they learn more skills, which are attractive to many employers. As a result, they are willing to increase earnings for those who have more education.
Still, in today’s labor market, there is a lot of discrimination that results in lower wages for many minorities. This is why sex, race, and nativity, were included in the model because these variables affect the outcome of an individual’s earnings. The number that is often quoted is that women typically make 20% less than men. This is due to a variety of factors that include discrimination, women are more likely to exit the labor force to care for their children, and women typically chose less lucrative jobs, such as teaching. They often choose these jobs because they know they might be treated poorly, in a field that is dominated by men (Garcia & Venek Smith, 2019). This puts female workers at a disadvantage in earning higher wages, no matter their schooling or skills. This study also observed and measured differences found between different races. Dummy variables for different races were created and included in the model to determine how and to what degree one’s culture and race impact an individual’s earnings. Even though immigrants typically adapt well, are successful in their new environment when they move to the U.S, and make significant contributions to economic growth, they still make less than U.S.-born workers when they first enter the U.S. For example, if an immigrant man were to come to the U.S at the age of 20, they are expected to earn about 15% less than a comparable native man (Borjas, 2020). Nativity was included in the model to make comparisons between those of U.S.-born citizens and those that are foreign-born.
Further, dummy variables for one’s marital status and place of work were included in the regression to examine if they significantly impact earnings. One might expect that marital status could impact earnings because if an individual is married, they might work harder to try and increase their earnings to provide for their family. However, if an individual goes through a tragic event like a divorce, separation, or their spouse dying, this could decrease their earnings because they need time to grieve and adjust to their new life. Also, the individual’s place of work was included in the model because some regions are known for paying higher wages simply because the standard of living is higher. If the cost of living is higher in a region, then employee will demand higher wages so they can live a comfortable life.
The sample was restricted to those who worked 32 hours or are considered full-time. Nonetheless, the usual number of hours individuals worked per week was included in the model because it is expected that those who work more will earn higher wages. For example, an employee that is only working 32 hours a week is not going to make close to the same salary as an employee working 40 hours or more a week. This differentiation in salary does not logically have to do with one’s education level, but is due to how much an employee is willing to work each week and how much their company demands of them to work each week. For instance, different places of work assign long work hours to all employees while other careers do not.
Finally, one of the biggest impacts when determining one’s returns to education is heavily based on a person’s field of degree. As seen in previous research, students are more likely to choose a field where they know they can be successful and make the most money. As a result, students studying these fields have a much higher likelihood of getting a greater return for their schooling. Therefore, the individual’s field of degree was included in the model to examine if individuals earned significantly higher wages compared to individuals with majors in a different field.
Results
Earnings regressions were performed for both females and males in the sample size. Table 3 shows the OLS output for females.
Table 3: Statistical Software Output for the OLS of the Earnings Model for Females
Dependent Variable: l_wagep
n = 169,699
| Coefficient | |
| const | 9.15754*** |
| agep | 0.0338860*** |
| sq_agep | -0.000332391*** |
| hs | 0.0989858*** |
| ged | 0.0493546*** |
| somecollege | 0.155420*** |
| associate | 0.220188*** |
| bachelor | 0.389273*** |
| master | 0.529400*** |
| professional | 0.674705*** |
| doctorate | 0.631128*** |
| foreign | -0.143346*** |
| black | -0.0167755** |
| americanindian | -0.0568918** |
| alaskanative | 0.00483228 |
| indianalaska | 0.0628921 |
| asian | 0.0812139*** |
| pacificislander | 0.00896915 |
| otherrace | -0.0182326 |
| mixedrace | 0.000668898 |
| married | 0.0996684*** |
| widow | -0.153157*** |
| divorce | 0.0156876** |
| separated | -0.209006*** |
| northeast | 0.196027*** |
| west | 0.165866*** |
| south | 0.00711527 |
| pacific | 0.111609*** |
| otherregion | -0.730986*** |
| wkhp | 0.00632629*** |
* significant at the 10% level
** significant at the 5% level
*** significant at the 1% level
First, age is a significant variable when predicting earnings for females. A coefficient of 0.033 indicates that wages increase by 3.3% with a 1-year increase in age, all else constant. Additionally, the negative coefficient on the “sq_agep” variable implies that female wages increase at a decreasing rate as they get older.
Next, individuals who have a high school diploma, GED, completed some college, associate’s degree, bachelor’s degree, master’s degree, professional degree, or a doctorate degree earn significantly more compared to individuals who have less than a high school diploma. For example, females earn 9.8% more with a high school diploma, 4.9% more with a GED, 15.5% more with some college credits, 22.0% more with an associate’s degree, 38.9% more with a bachelor’s degree, 52.9% more with a master’s degree, 67.5% with a professional degree, and 63.3% more with a doctorate’s degree compared to individuals with less than a high school degree, all else constant.
Also, the results show that foreign females earn significantly less than native U.S. females. In fact, they are estimated to earn 14.3% less than native females. Females from different races earn different wages compared to White females. For instance, Black and American Indian females earn significantly less than White females. The results show that Black females are predicted to earn 1.7% less and American Indian Females are expected to earn 5.7% less compared to White females, keeping all the other variables constant. However, Asian females are predicted to earn 8.1% more than White females, all else constant.
The OLS regression shows that a female’s marital status does significantly impact their earnings. In particular, a married woman is expected to earn 10.0% more and a divorced woman is expected to earn 1.6% more compared to single females. On the other hand, compared to a single woman, a woman who is a widow is predicted to earn 15.3% less and 20.9% less if they are separated.
Females who work in the Northeast, West, and Pacific regions significantly earned more compared to those who work in the Midwest region. The coefficients conclude that they earn 19.6%, 16.6%, and 11.2% more, respectively. Also, those who work outside of the U.S earn significantly 73.1% less than those who work in the Midwest region.
Finally, the results show that working more hours per week on average will increase your wages. Increasing your workweek by 1 hour increases your wages by 0.63%, all else constant.
To compare the results of the earnings for females to males, the same model was constructed, but only included males in the sample size. Table 4 displays the earnings model for males.
Table 4: Statistical Software Output for the OLS of the Earnings Model for Males
Dependent Variable: l_wagep
n = 194,097
| Coefficient | |
| const | 8.66136*** |
| agep | 0.0518313*** |
| sq_agep | -0.0005058470*** |
| hs | 0.1577210*** |
| ged | 0.05913610*** |
| somecollege | 0.2278710*** |
| associate | 0.2584820*** |
| bachelor | 0.4438670*** |
| master | 0.5387280*** |
| professional | 0.7253840*** |
| doctorate | 0.6093980*** |
| foreign | -0.1477960*** |
| black | -0.1098900*** |
| americanindian | -0.1163710*** |
| alaskanative | -0.1937540** |
| indianalaska | -0.09774580* |
| asian | 0.04763280*** |
| pacificislander | -0.0349745 |
| otherrace | -0.06952350*** |
| mixedrace | -0.04527780*** |
| married | 0.4835010*** |
| widow | 0.1375460*** |
| divorce | 0.1687340*** |
| separated | 0.03865070** |
| northeast | 0.1774990*** |
| west | 0.1528940*** |
| south | 0.03480160*** |
| pacific | 0.00869392 |
| otherregion | -0.6541270*** |
| wkhp | 0.00809398*** |
* significant at the 10% level
** significant at the 5% level
*** significant at the 1% level
Again, age is a significant variable when estimating an individual’s earnings when they are male. However, the effect is larger for males as a 1-year increase in age increases their wages by 5.2% compared to the 3.3% increase for females.
Also, educational attainment is still significant in the regression for males. Again, getting a GED has the lowest returns compared to not finishing high school, with a 5.9% increase in wages, and obtaining a professional degree had the highest return, with a 72.5% increase in wages. Once more, these increases are larger compared to females which implies that there is a wage gap between females and males.
Next, the regression results conclude that foreign-born workers earn significantly less than native-born males. A coefficient of -0.1477 implies that foreign males earn 14.8% less than native males, all else constant. Different from the female’s model, the male’s regression shows that Black, American Indian, Alaska Native, American Indian and Alaska Native tribes specified or American Indian or Alaska Native not specified, other races, and mixed races earn significantly less compared to White males. For instance these races are estimated to earn 11.0%, 11.6%, 19.4%, 9.8%, 7.0%, and 4.5% less compared to White males, respectively. Again, Asian males are expected to earn 4.8% more than White males.
No matter if the male is married, widowed, divorced, or separated, this increased their earnings significantly compared to being single. This is different from the females because only being married or divorced increased the female’s wages. Nonetheless, being married increased the male’s earnings by 48.4%, being widowed increased their earnings by 13.8%, being divorced increased their earnings by 16.9%, and being separated increased their earnings by 3.9% compared to single males.
Those working in the Northeast, West, and South regions are estimated to have higher wages compared to those males who work in the Midwest. Working in the Northeast region increased their earnings the most, with an increase of 17.7%. Next, those who work in the West are estimated to have higher wages of 15.3% and 3.5% when working in the South. Again, those who work in other regions outside of the U.S. can expect their wages to decrease by 65.4%. However, this decrease is not as large as it was for the females, which was a decrease of 73.1%.
Finally, working an additional hour each week increases males’ wages by 0.81%. Furthermore, this result is larger compared to the 0.63% increase for females, indicating a wage gap.
Now that the results show that obtaining more education significantly increases an individual’s earnings, another OLS regression was constructed to test if there are higher wage premiums associated with certain majors compared to getting a degree in Liberal Arts and History Fields. The sample size was again restricted to those who are between the ages of 35 and 55 and who worked more than 32 hours per week, on average. However, to compare those who have a college degree, the sample was restricted to those who had obtained a Bachelor’s degree or higher. Table 5 shows the results for the earnings model by field of degree for females who obtained at least a Bachelor’s degree.
Table 5: OLS of the Earnings Model by Field of Degree for Females Who Obtained At Least a Bachelor’s Degree
Dependent Variable: l_wagep
n = 73,746
| Coefficient | |
| const | 8.98884*** |
| agep | 0.0478679*** |
| sq_agep | -0.000471547*** |
| accounting | 0.891041*** |
| actuarial | 1.19677*** |
| agriculture | 0.662808*** |
| architecture | 0.880337*** |
| biology | 0.987403*** |
| business | 0.793334*** |
| chemistrypremed | 1.15543*** |
| communications | 0.789766*** |
| computer | 0.963870*** |
| earlyeducation | 0.517558*** |
| economics | 1.10672*** |
| othereducation | 0.597367*** |
| engineering | 1.11977*** |
| environmental | 0.708925*** |
| familyconsumer | 0.538182*** |
| finance | 1.01789*** |
| industrial | 0.640775*** |
| journalism | 0.856193*** |
| leisure | 0.709090*** |
| language | 0.692123*** |
| math | 0.939848*** |
| nursing | 0.947136*** |
| othernaturalscience | 0.761219*** |
| otherfields | 0.681235*** |
| politicalscience | 0.950127*** |
| pharmacy | 1.09971*** |
| physics | 1.08671*** |
| prelawlegal | 0.772235*** |
| protective | 0.617735*** |
| psychology | 0.696014*** |
| publicaffairs | 0.793286*** |
| othersocialscience | 0.768118*** |
| socialwork | 0.490485*** |
| sociology | 0.666948*** |
| technicalengineering | 0.869803*** |
| technicalhealth | 0.767223*** |
| visualarts | 0.583043*** |
| master | 0.140252*** |
| professional | 0.309233*** |
| doctorate | 0.219158*** |
* significant at the 10% level
** significant at the 5% level
*** significant at the 1% level
The dummy variable that was left out of the model was “liberalarts,” so the increases in wages are compared to those females who majored in a Liberal Arts or History field. Overall, the results show that every major that was included in the regression earns significantly higher wages compared to Liberal Arts and History graduates. However, the majors with the largest wage premiums for females are Actuarial (119.7% increase), Chemistry and Pre-Med (115.5% increase), Engineering (112.0% increase), Economics (110.7% increase), and Pharmacy (110.0% increase). The majors with the lowest wage premiums are Social Work (49.0% increase), Early Education (51.8% increase), and Family Consumer (53.8% increase). Even though these fields of degrees have the smallest wage premiums, it is important to note that they are still estimated to earn higher wages compared to those females who obtain a degree in a Liberal Arts or History field. Finally, including the dummy variables for the educational attainment, controls for the increases in wages that are strictly from obtaining a degree that is higher than a bachelor’s degree. Again, the results show that individuals who go on for a Master’s degree, Professional degree, or Doctorate, significantly have higher wages no matter their field of degree. Those who obtain a Professional degree, increase their wages the most with an increase of 30.9%, compared to individuals who only have a Bachelor’s degree.
Table 6 shows the results of the earnings model by field of degree for males who obtained at least a Bachelor’s degree.
Table 6: OLS of the Earnings Model by Field of Degree for Males Who Obtained At Least a Bachelor’s Degree
Dependent Variable: l_wagep
n = 77,145
| Coefficient | |
| const | 8.31128*** |
| agep | 0.0957277*** |
| sq_agep | -0.000951112*** |
| accounting | 0.848303*** |
| actuarial | 1.26290*** |
| agriculture | 0.479145*** |
| architecture | 0.646664*** |
| biology | 0.870998*** |
| business | 0.705764*** |
| chemistrypremed | 0.935833*** |
| communications | 0.587943*** |
| computer | 0.828899*** |
| earlyeducation | 0.280991*** |
| economics | 0.980099*** |
| othereducation | 0.329521*** |
| engineering | 0.926849*** |
| environmental | 0.557808*** |
| familyconsumer | 0.587615*** |
| finance | 0.965232*** |
| industrial | 0.529687*** |
| journalism | 0.531726*** |
| leisure | 0.459125*** |
| language | 0.563844*** |
| math | 0.835152*** |
| nursing | 0.645739*** |
| othernaturalscience | 0.682241*** |
| otherfields | 0.725912*** |
| politicalscience | 0.791348*** |
| pharmacy | 0.857299*** |
| physics | 0.829915*** |
| prelawlegal | 0.476631*** |
| protective | 0.492361*** |
| psychology | 0.542346*** |
| publicaffairs | 0.831867*** |
| othersocialscience | 0.527432*** |
| socialwork | 0.244718*** |
| sociology | 0.513607*** |
| technicalengineering | 0.702913*** |
| technicalhealth | 0.642385*** |
| visualarts | 0.342964*** |
| master | 0.107271*** |
| professional | 0.320669*** |
| doctorate | 0.145672*** |
* significant at the 10% level
** significant at the 5% level
*** significant at the 1% level
Again, the dummy variable that was excluded for the field of degrees is “liberalarts” As a result, the majors with the highest wage premiums compared to Liberal Arts and History major for males are Actuarial (126.3% increase), Economics (98.0% increase), Finance (96.5% increase), Chemistry and Pre-Med (93.6% increase), and Engineering (92.7% increase). While still earning more than students who obtained a degree in Liberal Arts and History fields, the majors with the lowest wage premiums are Social Work (24.5% increase), Early Education (28.1% increase), and other Education (33.0% increase). No matter the major that the individual declared, going back to school for a Master’s degree increases earnings by 10.7%, going back for a Professional degree increases earnings by 32.1%, and going back for a Doctorate increases earnings by 14.6%, compared to the individuals who only have their Bachelor’s degree.
Discussion
For both females and males, wages increase at a decreasing rate as the individual gets older. Through previous research, it is well-known that the age of an individual impacts their wages. Typically, younger individuals are less experienced compared to adults or even seniors. As a result, younger and less experienced workers have less bargaining power. On the other hand, firms are willing to increase wages for older workers because there is stronger competition among older workers. Additionally, as workers get older and stay with a firm, the firm is more likely to provide raises to incentivize the worker to stay. However, wages tend to increase rapidly for younger workers because they are starting from a lower base pay. Additionally, they still have a lot more to learn and can gain more human capital faster than older workers. For example, a fresh college graduate will be more likely to learn more skills at a firm in their first year at a position compared to an older worker who has been there for twenty years. As a result, the younger worker’s pay will increase larger than the older worker.
It is important to note that continuing any type of education increases an individual’s earnings compared to those who dropout of high school. However, obtaining a professional degree provides the most returns (67.5% for females and 72.5% for males). The first explanation for this would be the human capital hypothesis, which argues that additional schooling increases a person’s productivity. In other words, people learn skills and gain experience from school, which makes them more productive. Therefore, if an individual is more productive, then employers are willing to increase wages. A person who obtains a Professional or Doctorate should be the most productive because they have the most years of schooling, which is why they received the highest wages compared to high school dropouts. Another reason would correspond with the screening hypothesis. This states that employers are willing to pay higher wages to those with higher qualifications (more schooling) because this reduces their risk of hiring a person that does not have the capability of learning. Finally, employers are willing to pay higher wages to those who complete more schooling because obtaining a degree signals to employers that the individual has accomplished a task that is challenging. This implies that the individual had higher ability.
In both models, foreign workers are estimated to earn less than native workers. Typically, foreign workers earn less when they first enter the U.S. As time passes, their relative wages do increase. However, for many immigrant groups, the pay gap between foreign workers and native workers never closes. Some of this wage gap may be due to straightforward discrimination. Another possible explanation is that U.S. employers value education and training from other countries less than the education here. Therefore, the productivity at entry may be lower compared to native workers because there are educational differences (Anderson and Huang, 2019). As a result, firms may pay immigrants less because they believe they are not as capable as native workers because of the education or training that they have received. Also, the results show that there may be discrimination between races. Even though pay discrimination is illegal in the U.S., a racial wage gap still seems to exist. This is the result of how racial groups have been treated in the past. For example, the labor market in America has deprived Black workers of wages under slavery and hurt indigenous people through land theft. Essentially, these events resulted in lasting disparities in health, education, safety, and opportunities for those who come from minority backgrounds (The Simple Truth about the Gender Pay Gap: AAUW Report, 2021). Finally, this funneled minority groups into jobs that are low-paying and this trend continues today.
For both females and males, being married and divorced increases the individual’s earnings compared to being single. Being married or divorced might imply that there are children involved. As a result, this might drive the person to work higher-paying jobs so that they can provide for their family. However, for females being a widow or separated decreases their earnings. Obviously, losing a spouse would be a tragic event for anyone. However, society has created a stigma that “men should not grieve.” Instead, they should continue to provide for those around them, which is a possible explanation as to why being a widow increases a male’s earnings and decreases a female’s earnings. Women may take more time to grieve and adjust to the situation, decreasing their wages. Additionally, a separation typically occurs before a divorce. As a result, there is an adjustment period that a woman may experience that is similar to losing their spouse. However, by the time the divorce has become official, women might have had enough time to adjust and return back to work to their full capacity.
Females and males living in the Northeast region earned the most wages compared to those living in the Midwest region. As previously stated, the cost of living is a possible explanation for these differences. States that are a part of the Northeast region Have a much larger standard of living compared to states in the Midwest region. For example, compared to the national average of 100, New York state’s cost of living index is 120.5 while South Dakota’s is 88.3 (Cost of Living in New York). As a result, those living in New York will require higher wages to compensate for the difference in the cost of living.
For females, the majors with the highest wage premiums were Actuarial, Chemistry and Pre-Med, Engineering, Economics, and Pharmacy. Males have similar results, but Finance had a larger wage premium than Pharmacy. Actuarial, Chemistry and Pre-med, Engineering, and Pharmacy are majors that fall under STEM. Previous empirical work shows that students gravitate to STEM majors, especially during times of recessions, because they believe they can make more money (Blom et al., 2021). Students who major in technical fields, such as STEM majors, are shown to have the highest marginal return, which is consistent with these results. Additionally, in order to get a job in the STEM field typically requires more schooling than a bachelor’s degree. As a result, individuals may be paid more because they are also obtaining additional years of schooling in addition to their major choice.
Conclusion
Overall, the benefits of higher education also manifest in ways besides simply how much money they take home. The overall satisfaction broken down into a variety of different categories that comes with work seems to be higher for those who seek higher education, and health outcomes both physical and mental show better outcomes for those with higher education as well. There are undeniably better outcomes to many facets of life that come with higher education, bringing a fine amount of reasons to go for higher education when possible.
The findings of this study show that as schooling levels increase the earnings of individuals increases as well. When compared at the highest and lowest levels someone with a Doctorate showed the ability to generate more than 60% of the salary of an individual that never completed high school. Foreign workers, male and female, were also observed to earn significantly less than native-born workers. This large variety of races and origins all have proven to make less than White males and females by around 15%.
Marital status as well influences the return on education for all individuals. For women, it was proven that marriage positively impacts their earnings. Married women showed an increase of up to 10% in earnings once married and even divorced couples showed an increase of 1.6% compared to single women. However, women that are separated or widowed show opposite outcomes with the potential to lose almost 21% of their earnings. For males the results of marital status change. No matter if married, divorced, widowed, or separated, males still saw an increase in earnings. In comparison, when women saw an increase in salary of up to 10% once married, men saw an increase of up to almost 50%.
Further, it has been proven that men generally generate more revenue compared to women, causing a gender wage gap. As age increases one year, men and women both increase their earnings. However, it was determined that for men, as age increases one year that their wage increases by 5.2%. For women, their wages only showed an increase of 3.3%. Moreover, this study finalized the key points, being that men make more than women, marriage has a positive effect on earnings, native-born employees make more than foreign-born employees, and increased levels of schooling equal a higher return on education. Major choices as well have proven to impact the returns on education drastically. Students that study quantitative or technical fields showed to have the highest marginal returns. More specifically, STEM majors showed to generate such high salaries after college. Less desirable degrees in the eyes of the job market often had smaller returns. Liberal Arts and History graduates earned significantly lower wages compared to every other major included in this regression.
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