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By Victoria Poff, Corina Southworth, Valentina Solonos
The United States has a high level of income inequality. Part of the expanding wage gap can be explained by college major and occupation choices. When choosing a life path and profession, a person may unknowingly succumb to social stereotypes, perpetrated by his or her environment, family beliefs, educational institution, and the media. Throughout this chapter, we examine factors that have been distinctly identified as determinants of college major and occupation choice, and discuss how they relate to differences in income levels. Gender, racial differences, and family demographics all influence a person’s selection of college major which, in turn, affects their occupational choice and subsequent earnings. Our review of expert economic and behavioral studies reveals that gender, race, and occupation, individually and collectively, show repetitive patterns of having a significant effect on differences in earnings. In the sections discussing occupational choice, we use data from the U.S. Bureau of Labor Statistics to compare earnings between occupations that have high levels of homogeneity, as well as discuss observed variations in earnings within more diverse occupations. Occupational switching can also affect wages. In the section Occupational Switching, we seek to answer the question as to whether or not occupational switching has a positive or negative effect on wages and income inequality. Lastly, we share the results of our regression analysis using sample data from the National Longitudinal Survey of Youth 1979 to estimate the relationships between gender, race, and occupation on expected earnings.
Gender Differences in College Major Choice
Gender has been a main focus of studies on college major choice for many years now. Research suggests that there is a bias against women within the science, technology, engineering, and mathematics (STEM) fields. According to the National Science Foundation, women earn only about 20 percent of the bachelor’s degrees in engineering, computer science, and physics (St. Rose 1). Whereas, women earn 79 percent of bachelor’s degrees majoring in education (Planty et al. 1). This highlights the strong gender stratification across educational fields. Another study by Lisa Dickson, published in 2010, also found females, particularly Black and Hispanic females, to be widely underrepresented in engineering and computer science fields. Conversely, she finds Asian and White males are overrepresented in STEM majors in proportion to the gender distribution of students across all majors. STEM majors have long represented the largest separation by gender across six broad college major categories (refer to Figure 6.1). According to data collected by The National Center for Education Statistics for the 2016-2017 academic year, males continue to earn the majority of engineering and business degrees. These findings are consistent with those published by St. Rose and Dickson nearly a decade earlier.
Figure 6.1 Percentage distribution of bachelor’s degrees conferred by postsecondary institutions in selected fields of study, by sex: Academic year 2016–17
Source: “The Condition of Education – Postsecondary Education – Programs, Courses, and Completions – Undergraduate Degree Fields – Indicator May (2019).” IES NCES, The Institute of Education Sciences, May 2019, nces.ed.gov/programs/coe/indicator_cta.asp.
While considering students’ initial college major choice, approximately 25 percent of males choose a major in engineering and computer science, as compared to only 6 percent of females (Dickson 110). A survey done by the Higher Education Research Institute finds similar results. Even after controlling for SAT scores and other performance measures, Dickson finds that females are 16 percent more likely to major in natural and physical science fields than any other program of study (117). St. Rose observes that “first-year female college students are far less likely than their male peers to plan to major in a STEM field, a pattern that is consistent across race and ethnicity” (1). Dickson adds that “gender differences in major choice are much larger than racial and ethnic disparities” (108).
Women often feel as if they must have exceptional skills in math and science to pursue a course of study in STEM fields, but this mindset can be altered by changing how people view certain fields as suitable just for men or just for women (St. Rose 2). Research shows that women often access their abilities in male-dominated fields like math and science lower than men do (St. Rose 2). Correll also found that even when girls and boys have similar math and science grades and test scores, girls are more likely to access these abilities lower than boys (St Rose 2). Women often hold themselves to a higher standard, and feel as if they must have exceptional skills to pursue these male-dominant fields. This implies that women are less likely to go into STEM because they feel as if they do not have enough skill, and may be intimidated by the department’s climate. A change in social beliefs about what careers are so-called “male” and “female” would have a positive effect on how many women pursue STEM majors (St. Rose 2). Our society needs to help women gain confidence about going into STEM fields so they do not believe they are entering a male-dominant field with odds stacked against them. Another solution is to aid women in being able to accurately assess their abilities in subjects like math and science. “The more positively students assess their abilities in a subject, the more likely they are to enroll in classes in that subject or to choose it as their major” (St. Rose 2).
Women’s decisions to initially pick and remain within STEM majors can depend upon the atmosphere of the department (St. Rose 2). Women may not feel like they fit in with their male peers in STEM courses. It can be very intimidating for women to enter an all-male environment. The climate of the department can be a barrier to women’s recruitment and staying power in STEM fields if they feel they are outnumbered by the men and do not fit in (St. Rose 2). Small changes to improve the department’s atmosphere in STEM fields can have large gains in retaining and attracting more women into these majors. St. Rose also suggests that colleges should be actively recruiting women into these fields and broaden the scope of early coursework in STEM, such as offering more math and science classes. Having a supportive and stereotype free learning environment can have a positive impact on women’s performance. Teachers should set clear criteria for assessing students to avoid any biases or stereotypes (St. Rose 3).
An interesting aspect of Lisa Dickson’s work (similar to that of Jacobs 1995) is that she evaluates students’ propensity to switch majors during the course of their college career. She states, “not only are equally qualified women less likely to declare a major in engineering and computer science, but they are also significantly more likely to switch away from a major in that field” (119). Specifically, more than 40 percent of students who intended to major in engineering and science left the field. White females are 19 percent more likely to switch majors than White males. Reducing gender segregation at the college level, especially in STEM fields, is critical to further reducing the gender pay gap (St. Rose 3). Brown and Corcoran (1997) propose that the wage gap could be reduced by as much as 13 percent among college-educated men and women (Ma 212). St. Rose suggests one way to guard against gender segregation is to ensure colleges and universities comply with Title IX, which protects people in education programs from discrimination based on sex (3). Such is crucial for women to have fair opportunities within the STEM fields. They should also create a stereotype-free learning environment because negative stereotypes about females in math and science can have a big “impact on a woman’s performance through a phenomenon known as the stereotype threat ” (St. Rose 3).
Gender Differences in Occupational Choice
Discrimination of women in the labor market remains an issue in our country. Men have more prominence in the workforce. A gender unemployment gap also exists, where women are found to have higher unemployment rates. Children learn at a young age that society differentiates professions for men and for women. We find that people choose a profession that they believe aligns with these gender roles. Working women tend to be concentrated in lower-salaried occupations (Hecker 1995, Marini and Fan 1997). Gender roles play a large part in this. Often, there is a perception that boys are inherently good in math and physics, and girls in humanities. Women in Israel, Germany, the United States, and Japan have a lower probability of holding positions at the higher job levels in the workplace than do men. Following more traditional ideals, the female labor force is mainly concentrated in trade, education, and healthcare. The gender differences in occupations may be explained by the diverse personalities of men and women. It can also be explained by the presence of unique interests and preferences toward one profession or another, and the dominance of certain mental processes which might predisposition one to a certain occupation. When choosing a profession, men are guided by factors such as their prospects, career opportunities, and higher salaries. Some say the main motivation for men is their desire for power and independence. On the other hand, women are characterized by great emotionality and dependence. Women are motivated by experience, a sense of social significance, and interaction with other people. While there has been a convergence in terms of income and prestige by gender, studies show that differences in the fundamental values between men and women tend to orient women into occupations that emphasize the concern and well-being of others and social benefit.
Ultimately, the professional activity aspect dominates in men, and the socio-psychological aspect dominates in women. Women desire jobs with more flexibility, and often fall into low-wage occupations, such as cashier or waitress. Men, who are considered to be stronger and more capable, choose to work in more dynamic occupations. Employers might view men as being more capable of tolerating heavier workload, stress, and conflicts at work. Leadership and highly paid positions, such as captains of ships and aircraft are often held by men. Some companies believe that men are more reliable. Female leadership is underestimated, and firms may be skeptical about promoting women into these roles. Such preconceptions are changing though, and we are seeing more women in higher-ranking positions. The beauty industry has the highest number of women CEOs, despite rating high in gender inequality. In administrative positions, women are more likely to have roles in accounting, marketing, personnel services, and secretarial. Stereotypes about male exclusivity, higher qualifications, and a stable psyche are widespread in our society. Women face sexism and mansplaining at work. The situation is aggravated by a lack of female support groups and at work rivalries. Nevertheless, women should fight for decent pay and career advancement, and not settle for less. Self-confidence and a high level of professionalism are key to success.
Table 6.1 contains condensed labor force statistical data from the Current Population Survey published by the U.S. Bureau of Labor Statistics (BLS) in January 2020. It shows the number of salaried workers (in thousands) by occupation and sex. If there is gender equality, we expect to see nearly 50 percent men and 50 percent women represented in each of these broad occupational categories. Rather, we find that occupations like construction, maintenance and repair, and transportation are overwhelmingly male-dominant (more than 95 percent of the total workers are men). Women account for almost 80 percent of healthcare and personal care and service workers. Is this not how our society often portrays these roles? How often do you see a male childcare worker or a female truck driver? Even within the Professional sector there are rather large gender disparities in architecture, engineering, and education occupations.
Table 6.1 Pattern of Gender Distribution by Occupation
Source: www.bls.gov/cps/cpsaat39.htm
Recently, there has been a relaxation of societal gender stereotyping, and we have seen increased activity in women’s professional groups (women in leadership, women in business, etc.). More women are pursuing higher education, and this has led to a rise in women’s overall earnings from 59 cents per dollar earned by men to 77 cents for every dollar earned by men in 2008 (St. Rose 1). Referring back to the previous section about gender and college major selection, there is strong evidence that the choice of study in college directly contributes to occupational choice. Kimberly Shauman’s research in occupational sex segregation and the earnings of occupations reveals that “sex differences in college major explain 11-17 percent of the sex gap in relatively high-paying occupations” (Ma 212). The Institute of Women’s Policy provides a detailed assessment regarding gender wage gaps. They have calculated the ratio between median weekly earnings and gender for full-time workers which explains the percentage that women receive compared to men. In the first category is female earnings as a percentage of male earnings of the same racial group. In the second category is female earnings as a percentage of White male earnings. Out of White, Black, Hispanic, and Asian full-time workers, White females earn the highest – 81.5 percent of White men’s earnings. Asian female workers earnings are shown to be 18 percent higher when compared to White males. Wage differences are even greater when considering women of other races. African American women earn only 65.3 percent of the average wages of White men, and Latin American women receive only 61.6 percent. The median weekly earnings for Asian women is 93.5 percent of White male earnings (Holtzman et al. 3). The variances in women’s earnings depends on the combination of gender and race/ethnicity. In this case, Black women earn 89 percent of male earnings of the same racial group. Surprisingly, this is the highest value out of all the other racial and gender groups, and can be explained by educational differences. The main point is that female earnings, as a percentage of male earnings, are higher within the same racial group.
Let’s return to the data from the BLS Current Population Survey, and this time look at earnings differences by gender in those occupations where the representation of men and women is near equal (see table 6.2). In every occupation listed, the median weekly earnings of women are, on average, about 24 percent less than their male counterparts, and up to 45 percent less in the legal profession.
Table 6.2 Median Weekly Earnings by Detailed Occupation and Gender [Numbers in thousands]
Source: www.bls.gov/cps/cpsaat39.htm
Utilizing the expanded set of labor force statistics from the Current Population Survey, the graph below highlights the differences in pay between men and women within the same occupation (see Figure 6.2). Women tend to receive less compared to men, including occupations that employ the largest percentage of women; for instance, healthcare and education where the wage difference is between $50 and $300. The lowest difference of weekly earnings comes from office and administrative support occupations. It is possible that these jobs offer more flexible work schedules that have value to the employee and offsets compensation. On the other hand, the highest wage gap is found in professional and related occupations where the median weekly earnings difference exceeds $400.
Figure 6.2 Differences in Median Weekly Earnings Between Men and Women by Occupation
[in thousands]
According to Holtzman, Hegewisch, and Lacarte, “women’s median weekly earnings for full-time work were $789 in 2018 compared with $973 for men” (Holtzman et al. 1). Because women earn less than men, many choose to leave the workforce to start families. Women are more likely to choose a part-time job over a full-time one because they usually have other responsibilities such as childcare. Thus, women have fewer hours worked and fall behind when it comes to building a career. As a result, we see this widening gap in earnings between men and women (Holtzman et al. 1). A side effect of the wage gap is poverty, which has been shown to have a significant impact on minority women. Hispanic women earned an average of $617 per week for full-time work in 2018, and live at 130 percent of poverty. High poverty lowers a household’s weekly expenses and savings, leaving them vulnerable to emergencies that fail to be fully covered (Holtzman et al. 3).
Racial Differences in College Major Choice
The National Center for Education Statistics reports that Hispanics represent the lowest percentage of adults who have completed a bachelor’s degree or higher (15 percent as of 2016). They are followed by those identified as American Indians (15 percent) and Black (21 percent). Asian and White ethnicities represent the highest number of college graduates in the U.S. Several peer-reviewed articles capture similar findings. Among all males, African American males are more likely to enter college undeclared. Among females, more White females enter college undecided (Dickson 111). Keep in mind that White females are more likely to switch majors. Although, they remain highly underrepresented in STEM fields. Asian and White males are overrepresented in engineering and science. It is important to note that more attention is being given to examining initial versus final college major choices, and multiple researchers have found that graduates in engineering and natural sciences most often select these majors initially and retain this course of study to graduation, even though nearly half of all students switch majors.
The reason we focus on college major selection is because “educational decisions have strong implications for career aspects” (Ma 211). Social sciences and education are relatively lower-income occupations, and engineering and business are fields with higher earnings potential. Yingyi Ma of Syracuse University says that “focusing on the initial major is the first step in advancing our adequate understanding of the underrepresentation of women and minorities in sciences and engineering” (213). Understanding the racial differences in college major choice informs public and institutional policy to help address future labor market demand.
Racial Differences in Occupational Choice
Research also reveals statistically different earnings for different races and ethnic groups. In every age group, White Americans earn more, sometimes significantly more, compared to other ethnicities. Social stereotypes, racism, and discrimination in the workplace are contributing factors to wage differentials by race. These types of barriers to labor force participation often generate low self-esteem and low self-awareness in those targeted demographic groups. Career consultants and employers who treat workers of one race differently from those of another race are practicing discrimination. Racial discrimination does exist in the labor market and contributes to the large wage gap in this country. A person should be free to choose his or her professional path without such hindrance.
Understanding how racial and gender factors affect college major and occupational choices can be complicated. It is important to point out that economists who study these variables find that “racial and gender differences cannot be examined meaningfully in isolation from one another” (Ma 114). For example, Black females with college degrees are more interested in teaching and nursing occupations, while Asian females tend to concentrate on science and engineering professions. In many occupations, minority workers that enter the labor market are concentrated in lower-level positions. As a consequence, minorities remain in the lower earnings percentile even when work experience increases.
Family Influence
Choosing a college major is a process for most students, and one that involves time and effort, and the advice of parents, siblings, and other family members. Research consistently finds that family influence plays a crucial role in the education track. Career choices are likely to depend upon the structure of the family as well. Family priorities matter and this can affect one’s short and long-term decisions. Students have limited information about the labor market and wage differentials, and develop their earnings expectations from family members. Often the information that parents relay to their children, either directly or indirectly, is limited by their own human capital (skills, education, interests). Therefore, some economists believe that family social and human capital inequities can be transmitted from one generation to the next (Coleman 1988; Hoover-Dempsey and Sandler 1997; Ma 2009; Xia 2016).
In addition to family values and parental preferences, we observe that parents’ income and socio-economic status (SES) are also primary influencers. Those from a higher SES have quantitative advantages, such as parents’ years of schooling and income. Sometimes though, the qualitative differences, such as the quality of education and involvement in extracurricular activities, play a more significant role in decision-making. College students from more wealthy backgrounds tend to be more selective and more risk-averse to avoid downward social mobility. On the other hand, students from lower SES are often seeking upward mobility and are equally motivated to choose a major with higher earnings. This Relative Risk Aversion (RRA) theory, presented by Breen and Goldthorpe (1996), builds upon work done by Bourdieu (1984) and has been substantiated by other economists. For example, results from the Y. Ma model (2009) shows SES having a negative influence on choosing the fields of technical, life, and business and a positive on humanities and arts. Meaning, the higher SES the more likely the student is to select liberal arts, which is often a reflection of the family’s “cultural capital.”
Econometric Analysis
Thus far, we have discussed the relationships between gender, race, and socioeconomic status on a person’s decisions regarding which college major to select and in what industry to work. To further examine these relationships with respect to one’s annual earnings, we perform regression analysis on sample data from the U.S. Bureau of Labor Statistics’ National Longitudinal Survey of Youth 1997 (NLSY97). The NLSY97 is a cross-sectional sample of over 8,000 people living in the United States and born between 1980 and 1984. Participants have been interviewed eighteen times to date about their life experiences, including topics surrounding education, employment, family, and incomes. The most recent data release is from 2017-2018.
Data Description and Transformations
From this data, we capture a subset of 4,914 observations from respondents who reported wages within the 2016 calendar year, an occupation (aligned with the 2002 Census Occupation Codes), and some level of education. Males represent 2,515 sample observations (51 percent) and females represent 2,399 sample observations (49 percent). The model estimates the effects of the independent variables shown in table 6.3 on the dependent variable, the log transformation of Total Income From Annual Wages and Salary In Past Year. Many survey questions and responses are categorical. Occupation/Job Code is cross-referenced with the Bureau of Labor Statistics’ list of occupations defined as OES STEM – 100 occupations, including computer and mathematical, architecture and engineering, and life and physical science occupations. For observations where the NLSY97 occupation matches the BLS STEM list, the binary variable STEMOccupation is assigned as true. To perform regression, dummy variables are created to capture the effect of the qualitative variables for occupation, level of education, race, and gender. The new dummy variables are designated with a ‘D.’ As of the 2017 survey, about 37 percent of the sample population state GED/High School Diploma as their highest educational degree. Non-Black / Non-Hispanic respondents account for more than half of our sample population. Hence, GEDHighschool and NonBlackNonHispanic variables are omitted from the model to avoid the dummy variable trap.
Table 6.3 Variable Definitions
Summary Statistics
The mean annual income is $50,064. The mean age of the respondents is 35 years. Over 37 percent are college graduates, the majority of which are female. Nearly 30 percent of the sample identify as Non-Black / Non-Hispanic males. Black, Hispanic, and Mixed Race collectively represent 46 percent of respondents.
Table 6.4 Summary Statistics for Observations 1-4914
Estimation and Results
Based upon preliminary research, we predict that females earn less than their male counterparts, holding all other factors constant. Therefore, we expect to get a negative coefficient on the regressor GENDERIsFemale as a result of running Ordinary Least Squares (OLS) on the full sample range. Regression results on 4,914 observations show gender as significant in explaining the variation in earnings, but the coefficient of -0.45 is higher than anticipated. For this reason, we continue estimations separately – restricting the sample set to only males, then only females. The ordinary least squares estimations, including robust standard errors to correct for heteroskedasticity, are shown below (see fig. 6.3 and 6.4).
Model: logINCOME = β0+ β1CalcWorkExp + β2STEMOccupation + β3NoDegr + β4Assoc + β5Bachelor + β6Master + β7PhD + β8MD + β9Hispanic + β10Black + β11MixedRace + β12YOUTHDOESR01LIVEWITHBOTH
A report published in January 2017 by the U.S. Bureau of Labor Statistics, states that over 90 percent of STEM occupations had wages above the national average (Fayer et al. 6). Our analysis shows that both men and women in STEM occupations earn between 16 to 20 percent more, on average, than their counterparts in Non-STEM occupations. Having a college degree of any kind is statistically significant in relation to total income. One interesting observation from our results is that the magnitude of difference in income from holding an associate, bachelors, or masters degree as compared to a high school diploma is about the same for both men and women, but at the Ph.D. level females have a higher return on education. Occupations with the highest median earnings among women include physicians and surgeons, dentists, and engineering managers (Earnings). This is not to say that median earnings for females with advanced degrees is higher than males. Data reported by the U.S. Department of Labor and the U.S. Department of Education shows that, statistically, men earn more than women at all levels of educational attainment.
Asian and White Americans have the highest median earnings of any racial/ethnic group in the United States. NLSY97 limits racial descriptors to Black, Hispanic, Non-Black / Non-Hispanic, and Mixed Race. Therefore, we predict negative regression coefficients on the dummy variables with respect to our reference group, Non-Black / Non-Hispanic. At a 5% significance level, the p-values indicate that race may not have a substantial effect on the variation in income for women. Although, for men it appears that Black males have a lower income compared to their counterparts. The different results by gender might be explained by the fact that Black and Hispanic women have higher and more continuous labor force participation rates than Black and Hispanic men. In 2018, the BLS found that “among mothers with children under 18, Black mothers (78.4 percent) were more likely to be in the labor force than White (70.5 percent), Asian (65.0 percent), or Hispanic (61.9 percent) mothers” (Labor 2018).
As mentioned earlier, college major and career choices are often influenced by one’s parents or other close family members. Here we look at one parameter related to family structure to analyze the effect that living with both biological parents might have on future income. The observed impact is positive. Men and women who report living with both biological parents between the ages of 12-17 years old earn approximately 14 percent and 8 percent higher average incomes respectively. Future research using the NLSY97 data to evaluate family influence on total income may include variables such as parent’s income, father and mother’s highest grade completed, other non-biological household parental relationships, parents involvement in the military, or religiosity.
Figure 6.3 Gretl Model Ordinary Least Squares for Males
Figure 6.4 Gretl Model Ordinary Least Squares for Females
Occupational Switching
People are more likely to switch jobs by quitting their job to move up “the ladder,” rather than being laid off and becoming unemployed. Most find a new job and continue working rather than exiting the labor force. In the Chicago Fed Letter “Job Switching and Wage Growth” published by the Federal Reserve Bank of Chicago, the writers Jason Faberman and economist Alejandro Justiniano find that there is a very strong relationship between job switching and nominal wage growth. Using data on worker quits is a way to measure job switching within the U.S. Steven Davis, R. Jason Faberman, and John Haltiwanger use data from the Job Openings and Labor Turnover Survey (JOLTS) series collected in the early 1990s to show that the aggregate quit rates are procyclical. An economic quantity, like quit rates, that is positively correlated with the overall state of the economy is said to be procyclical. Therefore, job quits increase during an expansion and decrease during recessions. This is because people are more likely to switch jobs during an expansion when there are more job opportunities and “labor markets are tighter. A tight labor market implies that employers are more willing to offer higher wages to attract new workers” (Faberman and Justiniano 2). When jobs offer higher wages, there is a greater incentive for workers to quit their current jobs and take a new position to promote their careers.
According to Faberman and Justiniano, job quits decrease during a recession because unemployment rates are higher than normal, and there are fewer jobs available in the economy (2). Under these circumstances, people have less bargaining power for higher wages. Gadi Barlevy suggests “that this can create a ‘sullying’ effect of recessions, where workers become stuck in either low-quality jobs or jobs to which their skills are poorly matched because of the difficulty in moving up the job ladder during an economic downturn” (Faberman and Justiniano 2). It also reasons that because job quits are procyclical, wages would be too. Occupational switching typically entails a person moving to a higher-earning job. Given this, job quits and aggregate wages have a positive relationship, as job quits increase wages should also increase. Faberman and Justiniano state that wage growth is higher during expansions and this can be explained by job switching rates (quit rates) being higher. During a recession the quit rate decreases, meaning that there are fewer opportunities for new jobs at higher wages (2).
Figure 6.5 plots the quit rate against an alternative measure of wage growth using the average hourly earnings of production and nonsupervisory workers (Faberman and Justiniano 2-3). This graph shows that wage growth and the quit rate move together over time, highlighting the procyclical patterns of job quits and wage growth.
Figure 6.5 Quits and Wages, Production and Nonsupervisory Workers
Source: Faberman, R. Jason, and Alejandro Justiniano. “Job Switching and Wage Growth.”U.S. Chicago Fed Letter, Economic Research Department of the Federal Reserve Bank of Chicago, 2015, www.chicagofed.org/publications/chicago-fed-letter/2015/337.
Calculations given in table 6.5 show a very strong correlation between quit rate and wage growth. The results provide the measurement of the co-movement of between quit rate and average hourly earnings up to eight quarters in the future. The employment cost index (ECI) is used in the first column and shows the correlation between the quit rate and wage growth. The second column uses the average hourly earnings (AHE) to show the relationship between average hourly earnings wage growth and job quit rate. The strongest correlations occur using the ECI, the largest being 0.94 in quarter two. The strongest correlation using AHE occurs during quarter four being 0.78. According to Faberman and Justiniano these numbers suggest that fluctuations in the quit rate lead to fluctuations in the wage growth between a year to six months (3). We conclude that the quit rate can help to predict the wage growth rate.
Table 6.5 Job Quit Rate, Wage Growth, and Inflation Gap Correlation
Source: Faberman, R. Jason, and Alejandro Justiniano. “Job Switching and Wage Growth.”
U.S. Chicago Fed Letter, Economic Research Department of the Federal Reserve Bank of
Chicago, 2015, www.chicagofed.org/publications/chicago-fed-letter/2015/337.
People who are considered to be occupational switchers drive up wages as they begin to quit jobs and take new ones. Switching opportunities are always greater during an economic boom. The opposite is true during a recession. According to an article published by the Economic department for the Federal Reserve Bank of Chicago (2015), variations in the job quit rate can lead to changes in wage growth. This indicates that the “pace of job switching is a useful indicator for forecasting the behavior of wages” (Faberman and Justiniano 3).
Wage differentials may be explained by varying levels of occupation-specific human capital. Occupational mobility and wage inequality are interrelated because occupational mobility can affect human capital. This is because rises in occupational mobility and income inequality are caused by the rise in the variability of productivity shocks to specific occupations (Kambourov 32). Another possible explanation for the rise in wage inequality, according to Kambourov, is that it rises due to an increase in the productivity of more experienced workers (32-33). This implies that when the returns to experience within a job increase, then people will respond to that by investing more human capital and not switch their jobs as often. They could earn more within their current occupation by gaining more experience or education.
On the other hand, when job mobility and income wage inequality from job-specific experience and human capital investments become less relevant over time, there is a decline in the relative productivity of more experienced employees. When the importance of job-specific human capital decreases, occupational mobility increases, and there is a decrease in wage inequality because people can easily switch jobs without having an extensive skill set. Kambourov concludes that if the cost of occupational switching decreases over time, the wage inequality is lower than it would have been without job switching (33). When it is less costly for people to switch between jobs, and doing so results in higher wages, then income inequality goes down. The amount of human capital investment in job skills varies among certain jobs and people. According to Shaw, a portion of that occupational investment is easily applicable across various occupations (717). People seem to change their job to maximize the present value of their returns to this human capital investment (Shaw 717). This helps to explain why the wage inequality decreases when people can easily switch jobs.
Summary and Conclusions
Economists, sociologists, psychologists, and government officials are all keenly interested in better understanding the factors that contribute to income inequality within the United States. A portion of the ever-widening wage gap can be explained by college major and occupational choices. Throughout this essay, we have examined patterns and relationships between gender, racial differences, and family influences with an individual’s college major, occupational choice, and expected earnings. Our findings suggest a bias against women exists in STEM fields of study and occupations. A reason for this may be that women feel strongly intimidated, unwelcome, or undervalued in these male-dominated fields. Another reason may simply be that women are more naturally inclined toward occupations that emphasize the concern and well-being of others or that offer greater social benefit. Across most occupations, males earn more compared to their female counterparts. Female workers have a low impact on the gender wage gap. To ensure a positive increase in wages, the gender wage gap must be diminished. When earnings by occupation increase, those occupations become more attractive to students, and they may switch college majors to have potentially higher returns to education. STEM occupations continue to offer above-average wages and long-term job growth. Equalizing gender distribution across college majors, particularly STEM, is important to reducing the gender pay gap between college-education women and men.
The impact of occupational switching is known by calculating job quits. Job quits have a strong procyclical correlation with wage growth. During expansions, people often quit their jobs in pursuit of finding a higher-pay one. During recessions, unemployment is high and there is less movement. Yet, job switching does not necessarily lead to unemployment. Typically with job switching, workers earn a higher wage in the next job. When quit rates are low, there is not much potential for wage growth. Further research is needed to answer the question, how much does occupational switching affect individual earnings growth, because it can vary from job to job and from person to person?
Other actions to address the occupational gender and racial disparities might include monitoring employers’ compliance with anti-discrimination legislation, implementation of programs that stimulate female entrepreneurship, promotion of women into leadership positions, collection and dissemination of information on the state of the labor market, and ensuring women rights and equal opportunities with male workers. The implementation and use of these measures ensures equal opportunity, and can generate greater efficiency with a more diversified workforce. The U.S. can make progress in closing its significant wage gap by raising awareness through the active involvement by federal and state agencies in conjunction with educational institutions.
Bibliography
Antecol, Heather, and Kelly Bedard. “The Racial Wage Gap: The Importance of Labor Force Attachment Differences across Black, Mexican, and White Men.” The Journal of Human Resources, vol. 39, no. 2, 2004, pp. 564–583. JSTOR, www.jstor.org/stable/3559027.
Barlevy, Gadi. “The sullying effect of recessions,” Review of Economic Studies, Vol. 69,
- No. 1, Jan., 2002 pp. 65–96.
Bureau of Labor Statistics, U.S. Department of Labor. National Longitudinal Survey of Youth 1997 cohort, 1997-2017 (rounds 1-18). Produced and distributed by the Center for Human Resource Research (CHRR), The Ohio State University. Columbus, OH: 2019.
Brown, Duane. “The Role of Work and Cultural Values in Occupational Choice, Satisfaction, and Success: a Theoretical Statement.” Journal of Counseling and Development, vol. 80, no. 1, American Counseling Association, Jan. 2002, pp. 48–56, onlinelibrary.wiley.com/doi/abs/10.1002/j.1556-6678.2002.tb00165.x.
Davis, Steven J., R. Jason Faberman, and John C. Haltiwanger, 2012, “Labor market flows in the
cross section and over time,” Journal of Monetary Economics, Vol. 59, No. 1, Jan. 2012,
- 1–18
Dickson, Lisa. “Race and Gender Differences in College Major Choice.” The Annals of the American Academy of Political and Social Science, vol. 627, Sage Publications, Jan. 2010, pp. 108–24.
“Earnings | U.S. Department of Labor.” Women’s Bureau, U.S. Department of Labor, www.dol.gov/agencies/wb/data/earnings. Accessed 4 May 2020.
Faberman, R. Jason, and Alejandro Justiniano. “Job Switching and Wage Growth.” U.S. Chicago Fed Letter, Economic Research Department of the Federal Reserve Bank of Chicago, 2015, www.chicagofed.org/publications/chicago-fed-letter/2015/337.
Holtzman, Tessa, and Ariane Hegewisch. “The Gender Wage Gap: 2018 Earnings Differences by Race and Ethnicity.” Institute for Women’s Policy Research, 1 June 2019, iwpr.org/publications/gender-wage-gap-2018/.
Kambourov, Gueorgui, and Iourii Manovskii. “Occupational Mobility and Wage Inequality.” The Review of Economic Studies, vol. 76, 2, Apr. 2009, p. 731–759, https://doi.org/10.1111/j.1467-937X.2009.00535.x.
Planty, M., W. Hussar, T. Snyder, G. Kena, A. KewalRamani, J. Kemp, K. Bianco, and R. Dinkes. 2009. Condition of Education 2009 (NCES 2009-081). Washington, DC: U.S. Department of Education, National Center for Education Statistics.
“Labor Force Characteristics by Race and Ethnicity, 2017.” BLS Report 1076, U.S. BUREAU OF LABOR STATISTICS, Aug. 2018, www.bls.gov/opub/reports/race-and-ethnicity/2017/home.htm.
Ma, Yingyi. “Family Socioeconomic Status, Parental Involvement, and College Major Choices—Gender, Race/Ethnic, and Nativity Patterns.” Sociological Perspectives, vol. 52, no. 2, University of California Press, June 2009, pp. 211–34, doi:10.1525/sop.2009.52.2.211.
Shaw, Kathryn L. “Occupational Change, Employer Change, and the Transferability of Skills.” Southern Economic Journal, vol. 53, no. 3,, pp. 702–719. JSTOR, 1987. www.jstor.org/stable/1058765.
St. Rose, Andresse. “STEM Major Choice and the Gender Pay gap (Featured Topics).” On Campus with Women, vol. 39, no. 1, Association of American Colleges and Universities, Mar. 2010.
U.S. Bureau of Labor Statistics. “Median Weekly Earnings of Full-Time Wage and Salary Workers by Detailed Occupation and Sex.” Labor Force Statistics from the Current Population Survey, 22 Jan. 2020, www.bls.gov/cps/cpsaat39.htm.
U.S. Bureau of Labor Statistics. “OES Topics.” STEM Definition, 3 Feb. 2020, www.bls.gov/oes/topics.htm#stem.