The existence of racial inequality in the United States is exposed to many who are affected negatively in their income. While there is a general consensus among research that nonwhite individuals with a bachelor’s degree are exposed to discrimination influences on their earnings, gaps in past literature and datable methodology leaves much to be anticipated. Kiefer and Philips (1988) introduced this positive correlation between race and income inequality with the human capital theory. They believed that factors outside the labor market influence racial inequality and focused on the quality and quantity of school for blacks during the 1980’s. They were not focusing on the educational attainment of college graduates.
Since then, their theory of human capital has been supported by numerous studies (Weinberger, 1998; Daymont and Anderisani, 1984; Roksa and Levey, 2010; Lang, and Lehmann, 2012; Akee, Jones, and Porter, 2017). However, the magnitude of this effect, and the methodology best used to analyze the effects of income discrimination, continues to be debated. Also, few studies have focused specifically on analyzing how race impacts the income of individuals with a bachelor’s degree. My study aims to overcome these shortfalls by avoiding methodology mistakes from past literature while specifically focusing on race in college graduate’s income.
The impact of race on college graduates continues to affect income, and how to measure this effect, is to open to debate among past literature. Lang and Lehmann (2012) reviewed principal models of race discrimination in the labor market and studied their ability to explain the broad empirical regularities with respect to wage. Their focus was exclusively on the differential labor market experiences of black and white men. They found that the Black and White male earnings ratio has declined at 0.8 after years of convergence (Lang and Lehmann, 2012).
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This is consistent with Weinberger (1998) findings that women and black men have lower average earnings than white men with the same number of years of education. She studied race (Hispanic, White, Black and Asian) and gender wage gaps in the market for recent college graduates, however, her limitation of her analysis is that it studied wage differentials in only a small sector of the economy. My study will avoid this limitation and analyze a larger sample size of college graduates. While she focused on the races White, Black, Asian, and Hispanic, I will also study these races as variables to control for income discrimination.
A common analytical strategy in the literature is to divide college majors into a few broad categories and examine their connection with wages. Roksa and Levey (2010) present evidence of income differentials among students majoring in different fields of study. This suggests that depending on college graduates’ major they can be exposed to income discrimination as they transition into the labor market and their occupational trajectories. Daymonet and Anderisani (1984) demonstrated that there are greater payoffs for women than for men for traditionally female majors like health or biology and education and greater payoffs for men than for women for some traditionally male fields like engineering. However, their study was conducted from data in National Longitudinal Studies of the High School Class of 1972 (NLS72), which presented strong evidence but not significant enough to understand the race income discrimination in the labor market.
Similarly, Heathcote, Storesletten and Violante (2010) used a neoclassical model to study the labor production on households who have at least a bachelor’s degree and found that from 1970 to 2000 males college wage premium ratio was 1.6 and females college wage premium ratio was 0.67. They also presented that the gender wage gap narrowed was concentrated in the earlier period, whereas the respondents with college degrees had experienced growth in the skill premium during the 1980s and 1990s.They admit that income inequality will increase in the future as more individuals seek education across their career paths. I am going to expand on this study by focusing on individuals race and education instead of households and gender.
Another topic of debate is changes in income inequality and income mobility by race for economic policy and analysis. Blacks, American Indians, and Hispanics are consistently at the low end of the total income distribution compared to Whites, Asians, and those in the Other group (Akee, Jones, and Porter, 2017). In comparison to past studies, Kiefer and Philips (1988) argued that black males are not promoted as rapidly as whites because blacks are systematically confined to labor market segments which lack opportunities for advancement, and because promotion procedures are themselves discriminatory.
Akee, Jones and Porter (2017) are one of the first to conduct an analysis of income inequality and mobility by race and ethnic origin using restricted-use data that included, income from the U.S. tax filers and over a 15 years’ time frame. Their results showed that there is a positive correlation between race and income however, income inequality proved to be largest across race and ethnic groups at the uppermost ends of the income distribution. They admit that their study contains some shortfalls while using a record-linkage approach to link the data because it introduces some bias within minorities. Therefore, my study will analyze data that is not bias focusing on four races instead of seven.
Despite these differences, the socio-demographic factors of the participants included in most of the past literature are the same. Previous studies have focused only one white and black college graduates (Lang and Lehmann, 2012; Weinberger, 1998). This is done in order to compare the recent studies to past literature. However, their reports are focused on the gender gaps, and level of income, I will be focusing my cross-sectional analysis on how race impacts college graduates’ earnings. I intend to have race as my dummy variable in order to update past literature and limit unnecessarily restrictions.
Also, income discrimination was accounted for several years ago (Kiefer and Philips, 1988; Roksa and Levey, 2010; Weinberger, 1998; Daymont and Anderisani, 1984; Roksa and Levey, 2010). In my study I will use the most recent data from American Community Survey 2017. For most literature, income discrimination is assumed to be exposed to individuals that are in certain occupations and majors, but also by education attainments. However, human capital theory states that any improvement in education will increase income, and differences in work preferences and work expectations are important in understanding race differences in earnings. For this reason, I will consider all occupations and focus on individual’s income that have a bachelor’s degree.
Although past studies have improved the analysis of race impacts on income, there continues to be a large gap in literature. Few studies have focused specifically on how race impacts future college graduate’s income. Among these studies that do focus on race and education, there are differing results. Weinberger (1998) found that Hispanic men have the highest average hourly earnings ($10.02), followed by Asian men, white men, Asian women, Hispanic women, black men, white woman, and black woman ($7.08). Akee, Jones and Porter (2017) found that Whites tend to have an excessively large share of income in top quantiles, while all other races accumulate a disproportionately large share of income at the bottom 10 percent and 1 percent of the overall income distribution. These differences in results are due to the widely varying methodology used for each study.
My study is going to expand on this literature and provide broader variables, such as race and occupations. I am going to provide more accurate variables and specifically focus on how race impacts college graduate’s income. I am going to avoid looking at bias data sets and focus on a larger more recent sample size of individuals instead of households. By combining the most proven methodology from past literature and applying it to how race can negatively affect a college graduates’ income, my study will contribute in areas that no past literature has explored.
