Returns to Tertiary Education and the Differences by Gender, Race, Citizenship, and Occupation

Returns to Tertiary Education and the Differences by Gender, Race, Citizenship, and Occupation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Returns to Tertiary Education and the Differences by Gender, Race, Citizenship, and Occupation Adugna Olani, Eryn Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6866499/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Studies that quantify the wage premium to different levels of tertiary education and compare them across people of different sex, races, citizenship statuses, and occupations are rare. In this paper, we compare the average yearly income margin of different levels of tertiary education, irrespective of major or attended school, to that of a person who completed only secondary education. We focus on the following levels of tertiary education: college without a degree, an associate, bachelor’s, master’s, doctoral, and professional degrees. Using micro data from the US Census Bureau from the years 2014 to 2019 and the Mincer equation, we estimate a regression for each gender, race, citizenship, and occupation category. The earnings premium for the different levels of tertiary education, each compared to the earnings of someone with no tertiary education, are as follows: less than a year of college without a degree: 3.8; some years of college without a degree: 5.8%; associate: 11.3%; bachelor’s: 34.6%; master’s: 51.5%; doctoral: 68.6%; and professional: 71.3%. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The decision to invest in higher education has always been an important one in the United States, where many people opt for a bachelor’s degree and beyond. Given the high costs of higher education, it is important for people to understand the economic returns to the different levels of tertiary education. In this paper we quantify the average yearly premium to the increased income margin of tertiary education for years 2014 to 2019 using the Mincer equation. The premium stated for each level is the increased yearly income margin compared to the average return for someone with no tertiary education. The levels include less than a year of college with no degree, some years of college with no degree, an associate, bachelor’s, master’s, doctoral, and professional degree. In addition to the financial reward for each degree type, this paper will further examine the wage gap between men and women, people of different races, citizenship statuses, and people in different occupations 3 . Unlike studies that calculate a present value for the return to higher education, this paper only provides the average yearly reward; the cost of education is not accounted for, and income growth rates are not calculated. While there are countless articles that examine returns to an additional year of schooling, fewer focus on higher degrees, and few comprehensively compare the earning potential at each level of tertiary education. There are also few papers that comprehensively compare the returns to people under several different social categories within a single study. Our goal is to estimate how much someone can expect to earn on average annually above a high school diploma, controlling for their characteristics and tertiary educational attainment. This estimation informs someone of the average annual value of their income earnings and allows someone to estimate this value irrespective of their life expectancy. There are two predominant theories in literature explaining the increase in income as years of education increases: the signaling theory and the human capital theory. Both examine the relationship between the years of education and subsequent earnings potential, but the two have different theoretical explanations for the increased earning potential. However, both imply a positive causal relationship between education and income. The human capital theory is widely regarded to originate from Theodore Schultz. This theory explains the education-wage relationship as a causal one; as someone becomes more educated, they increase their productivity and skills (Schultz, 1961). Employers then reward these more productive individuals with higher wages in the market. The signaling theory is usually attributed to Michael Spence. Contrary to the human capital theory, the signaling model claims that educational attainment is a signal of a worker’s innate productivity and not an enhancer (Spence, 1973). Page (2010) states the following assumptions for the theory: (1) workers have differing innate levels of ability not affected by education; (2) higher educational attainment incurs higher costs, including psychological costs, which are higher for those of low productivity and lower for those of high productivity; (3) individual workers know their true level of productivity and employers do not; (4) schooling levels can be observed at no cost. Because employers cannot directly observe a worker’s innate ability, educational attainment becomes the signal that employers use to estimate a worker’s true ability. Worker productivity must be negatively related to the cost of acquiring the education signal, and the cost differences that arise between high and low ability workers must arise from differences in cognitive ability alone, and not from outside influences such as differences in financial situations. Although the signaling model may include different assumptions, it does not exclude the possibly for human capital accruement (although human capital models require it). Both theories assume that people must behave rationally and invest in education as long as the marginal benefit of an additional year of schooling is greater than the marginal cost. Weiss (1995) hypothesizes that the two theories may not be mutually exclusive. There have been several studies testing the plausibility of the competing theories, but few studies have convincingly argued for either. This may be because different educational pathways carry one effect more than the other. For example, a technical pathway, such as engineering, may experience a greater human capital effect since their education often involves developing skills that are directly applied in its particular field. On the other hand, a liberal arts education does not specialize in technical skills but rather aims to develop a broader spectrum of knowledge; the skills and accomplishments demonstrated here signal future success. Thus, comparing outcomes for people in liberal arts pathways versus those in engineering or technical pathways may help clarify the differences between the two theories. In sum, these theories suggest differing effects of education on ability, but both imply a positive causal relationship between education and income. In existing literature, this positive correlation between schooling and earnings potential exists as well. Barro (2010) found that an additional year of schooling equates to a 5% to 12% increase in earnings, which is consistent with most Mincerian estimates in literature. Goldin (2004, 2006) discovered a positive correlation between income and college education by examining the narrowing gender wage gap in early 1980 when women increased their college attendance and entering of the labor force. This paper will estimate this relationship between schooling and earnings potential between men and women, between White, Black, and Asian Americans, between US-born, naturalized, and non-citizens, and between management, business, health, legal, computer, and engineering occupations. This paper’s data is from the American Community Survey, a demographics survey conducted by the US Census Bureau. All persons in the dataset with an educational attainment at and above high school for the years 2014 to 2019 were used for our analysis. We use the Mincerian approach of explaining wages by regressing the logarithm of yearly wages of each individual on a vector of all tertiary educational attainment levels and a vector of control variables such as age, sex, race, and more (see Appendix 1) to estimate the quantified returns to higher education. We compare the average yearly income margin of different levels of tertiary education to that of a person who completed only secondary education, which is a high school diploma. Our results show that earnings potential increases with a higher level of tertiary education. The yearly earnings premium for the different levels of tertiary education, each compared to the earnings of someone with no tertiary education, are as follows: less than a year of college without a degree: 3.8%; some years of college without a degree: 5.8%; associate: 11.3%; bachelor’s: 34.6%; master’s: 51.5%; doctoral: 68.6%; and professional: 71.3%. For an associate, master’s and doctoral degree, the yearly premium is nearly equal between men and women. However, women earn 10% less than men at the professional level. Across all levels, Black people earn less than White and Asian people. The gap widens at higher degrees. Non-citizens also consistently earn less than citizens at each level. This, however, may be due to visa work restrictions and language barriers. Occupational differences are relatively consistent with other findings, with the exception that all occupations except healthcare workers earn more at the doctoral than the professional level. 2. Related Literature This paper follows the extensive literature on the topic of returns to higher education. Many studies have examined the returns to a college degree. Most researchers, such as Carnevale et al ( 2011 ), use a present value calculation. He found that an adult in 2009 with a bachelor’s degree earned 84% more over a lifetime than a high school graduate did. Hout ( 2012 ) used the US Census Bureau data and calculated that in 2002, within a forty-year work life, men with college degrees can expect to earn $ 2,955,000 over their lifetime as opposed to $ 1,664,000 with a high school diploma; the college degree can earn its costs several times over. This paper chooses to look at annual returns as opposed to a lifetime of earnings because it is not well-studied within literature, and this paper’s goal is to quantify the wage/salary premium at each level of tertiary education. The high return to tertiary education exists in cross-country studies as well. Boarini and Strauss ( 2010 ) found that from 1991 to 2005, the returns to an additional year of tertiary education are on average 8% and range from 4–15% depending on the country. Studies have also found that large wage premia differences across countries exist. The gross wage premium is 27% for Spanish men and nearly 90% for Hungarian and US degree holders. The per annum premium is 17% for men and women in Hungary and the United States (Strauss & Maisonneuve, 2009). Although the returns to higher education are positive, they differ between different people. Maurin and McNally ( 2008 ) found the largest returns to college were for those who would have been rejected but were instead accepted to a school; the average student’s returns are lower compared to marginal students. This is not to say, however, that the returns for the average student are not positive. Many studies have also studied whether an elite or high-quality college has a higher premium than an average school. Evidence of a higher wage reward from elite colleges have been mixed. Dale and Krueger ( 2002 ) compared college freshmen who were admitted to a selective university to those who were admitted to a less selective one and the elite college but enrolled at the less selective university. The students in the latter group earned as much as those enrolled at the elite school. Other studies have found a statistically significant difference in premium from enrolling at an elite school. Hoekstra (2009) studied students enrolled in a selective state university just above the admissions cutoff and students right below the cutoff who attended a less selective university. They found that attending the most selective state university increased earnings 20% for white males. The gender wage gap has been less studied in recent years due to evidence of its closing. McDonald and Thornton ( 2007 ) studied this gap by selecting college graduates newly entering the workforce to control for educational levels and job experience. They found that in 2006, women earned about 90% of what men earned, but 90% of this gap could be explained by differences in major choices. The racial wage gap, on the other hand, still exists. Many studies have found evidence of this phenomenon. Studies conducted on wage differentials between White, Black and Hispanic people have found that overall, without any controls, White people earn about 5% and 6% more than Black and Hispanic people, respectively. However, even after adding controls such as education and citizenship, unexplained differences still existed, which suggested systemic racial discrimination, especially towards Black individuals (Kamara, 2015 ). Fewer studies have examined returns for individuals of different citizenship status. Sumption and Flamm ( 2012 ) found that naturalized citizens tend to earn more than noncitizens for reasons such as higher education, better English ability, lower unemployment chance, and more work experience. However, even when these factors are controlled for, naturalized citizens are found to still earn at last 5% more than noncitizens, which suggests there is a citizenship premium. Furthermore, immigrants pursuing graduate degrees are slightly more likely to be in the lowest income quintile than the average student. Graduate immigrant students are also more likely to have dependents, which adds additional financial pressure (NCES, 2004). Occupation and higher education are highly related. As someone obtains schooling beyond a bachelors, they increasingly specialize and select themselves into a particular work industry. Hence, it is important to examine the returns for different occupations. Oreopoulos and Petronijevic ( 2013 ) discovered that the wage premium to tertiary education is higher than that of high school but varies significantly across different occupations; returns depend on education and occupational choices. This implies that college major choice is an important consideration for future returns. Carnevale et al ( 2011 ) found lifetime earnings to a bachelor’s degree are highest in the managerial, health professional, and science, technology, engineering, and mathematics occupations. Altonji and Zimmerman ( 2017 ) had similar findings when examining the estimated net returns of students in different fields of study at Florida public universities. They found that the industries with the highest net returns were engineering, computer science, business, and health. 3. Data and Methodology 3.1 Dataset The data used in this paper is a subset of the American Community Survey, a yearly individual and household census conducted by the US Census Bureau. The publicized data is a random sample of 1% of the US population from the years 2005 to 2019. A person surveyed once may not surveyed again for the next upcoming five surveys. For this study, all persons in the dataset with an educational attainment above high school diploma for the years 2014 to 2019 were used for analysis. This averages about 1.3 million individuals per year. The independent variable in this study is educational attainment level and the wage/salary income in the last annual year is used to measure the returns 4 . The educational levels examined in this study are some college education but less than one year of college, one or more years of college credit with no degree, an Associate degree (e.g., AA, AS), Bachelor’s degree (e.g., BA. BS), Master’s degree (e.g., MA, MS, MEng, MEd, MSW, MBA), Doctorate degree (e.g., PhD, EdD) and Professional degree beyond bachelor’s degree (e.g., MD, DDS, DVM, LLB, JD). The educational level assigned to each individual is their highest level obtained. For example, a person who completed a bachelor’s degree and a MD would be reported as having a professional degree. The variables sex, race, citizenship status, and occupation are used to measure the different outcomes of tertiary education on these groups. Specifically, this paper examines the wage gap between men and women, the average yearly premium difference between White, Black and Asian Americans, the average yearly return difference between US-born citizens, naturalized citizens, and noncitizens, and the return difference between people in management, business, health, legal, computer, and engineering occupations. Additional controls such as a person’s age, age 2 to account for experience, marital status, same sex households, English ability, disability status, annual weeks and hours worked, class of worker, family income, and quarter of birth are included to account for factors apart from education that may affect income. See Appendix 1 for the full variable list and descriptions. Hispanics account for a significant proportion of the US population at 18% of the total population. They are not included in this estimation because the US Census Bureau codes Hispanics differently than the other race categories. This makes comparing across race groups complicated. However, this is not to underestimate the importance of Hispanics in the US. 3.2 Methodology To measure the financial reward to each level of tertiary education, this paper will use the Mincerian estimation by regressing the logarithm of yearly wages on a vector of the levels of tertiary education. The following model is used to estimate the relationship between educational attainment on income: Y = βo + Xβ + W Γ + ε (1) Where Y is the logarithm of yearly wages/salary income per individual, βo is the constant, X is a vector of the levels of tertiary education, and β is a vector of the coefficients of the levels of education, which represents the percent increase in wage compared to someone with just a high school diploma. W is a vector of the control variables, Γ is a vector of the coefficients of the control variables, and ε is the error term. Model (1) captures the averaged effect of each obtained level of tertiary education on wage returns for everyone who has an education beyond high school. Since the wage variable from the database has an upper limit of $999,999 earned annually, upper outliers are excluded from the analysis. The same model is used to compare returns between different genders, races, citizenship statuses, and occupations. In each case, the model will only be run on individuals that fall under each category. For example, for females, equation (1) is estimated by dropping gender as a control variable and is estimated for females only. 4 Results 4.1 Results on general returns to tertiary education For the annual average returns of different levels of tertiary education from 2014 to 2019, a regression of model (1) is run on individuals with an educational attainment above high school in these years. The coefficients displayed at each level represents the percent increase in wage from the average individual with no college education. The results are shown in Table 1 . A graphical comparison of the returns is displayed in Fig. 1 . Table 1 Average Yearly Premium of Tertiary Education 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .063*** (.002) .063*** (.002) .067*** (.002) .065*** (.002) .065*** (.002) .026*** (.002) .058 Associates .122*** (.002) .114*** (.002) .115*** (.002) .107*** (.002) .107*** (.002) .114*** (.003) .113 Bachelors .347*** (.002) .339*** (.002) .349*** (.002) .345*** (.002) .349*** (.002) .347*** (.002) .346 Masters .515*** (.003) .516*** (.003) .520*** (.003) .515*** (.003) .519*** (.002) .502*** (.003) .515 Doctoral .673*** (.005) .684*** (.005) .700*** (.005) .691*** (.005) .685*** (.005) .685*** (.006) .686 Professional .701*** (.005) .711*** (.005) .723*** (.005) .718*** (.004) .731*** (.004) .694*** (.005) .713 Sex yes yes yes yes yes yes Age yes yes yes yes yes yes Age 2 yes yes yes yes yes yes Race yes yes yes yes yes yes Citizenship yes yes yes yes yes yes Marital Status yes yes yes yes yes yes Same sex household yes yes yes yes yes no English ability yes yes yes yes yes yes Disability yes yes yes yes yes yes Occupation industry yes yes yes yes yes yes Class of worker yes yes yes yes yes yes Working hours yes yes yes yes yes yes Weeks worked yes yes yes yes yes no Family Income yes yes yes yes yes yes Adj. R 2 0.687 0.685 0.678 0.674 0.668 0.520 N 1,358,850 1,223,935 1,389,000 1,418,979 1,439,482 1,458,479 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. The variables weeks worked and same sex household were no longer in the 2019 ACS dataset and dropped from the 2019 regression. Figure 1 displays the coefficients under the average column in Table 1 on a bar graph. The levels of tertiary education are displayed on the horizontal axis and shows the increase in wage margin relative to someone with just a high school diploma on the vertical axis. Average values in Table 1 represent the average returns across the six years for the given level of education. Conducting a pooled regression across all six years will provide similar results as the average values. The wage premium increases with higher tertiary educational attainment. The returns are consistent across the years with the exception of college with no degree in 2019. This is because the weeks worked control variable was not recorded in 2019 and not because of any actual changes in the returns 5 The returns to a professional >. These results suggest that weeks worked matter in precisely estimating the returns because some people work overtime without vacations. Because returns to college with no degree fell after weeks worked was dropped, it suggests that people without a college degree but with some amount of college education tend to work more weeks compared to other people. This is why dropping the variable has the largest effect for this group. The results from existing literature tend to support higher returns than our results (Carnevale et. al., 2011 , Boarini & Strauss, 2010 ) but this may be because they are estimating a lifecycle of earnings, which account for income growth rates 6 . The returns to a professional degree may actually be higher than what is observed here because other monetary and nonmonetary benefits are not included in yearly salaries. These include bonuses, private health insurance, and other working benefits that are often provided in occupations that require a professional degree. By comparing the returns to a college education greater than one year but with no degree to that of a bachelor’s degree, we find evidence of the signaling theory. The return to a bachelor’s degree (.347) is more than four times than that of more than a year of college with no degree (.058). A college education may be creating human capital, but if the human capital effect was the one in effect, we should observe someone with a bachelor’s degree earns more than four times the wage of a person with more than a year of college education. This suggestive of signaling theory as the bachelor's diploma is used as a signal. 4.2 Results on returns based on Gender For the average returns for men from 2014 to 2019, a regression of model (1) is run on all men in the dataset. The results are shown in Table 2 . These steps are repeated for all women in the dataset to find their average returns from these years, presented in Table 3 . Figure 2 shows a graphical comparison between the returns for each sex against the general returns. Table 2 Average Yearly Premium for Men 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .055*** (.003) .063*** (.002) .063*** (.003) .063*** (.003) .064*** (.003) .024*** (.003) .055 Associates .108*** (.003) .101*** (.004) .102*** (.003) .094*** (.003) .092*** (.003) .103*** (.004) .100 Bachelors .340*** (.003) .339*** (.003) .347*** (.003) .345*** (.003) .351*** (.003) .351*** (.003) .346 Masters .489*** (.004) .504*** (.004) .500*** (.004) .498*** (.004) .508*** (.004) .485*** (.004) .497 Doctoral .675*** (.007) .692*** (.007) .705*** (.007) .689*** (.007) .675*** (.007) .667*** (.008) .684 Professional .735*** (.007) .767*** (.007) .768*** (.007) .755*** (.007) .766*** (.007) .743*** (.008) .756 Adj. R 2 0.667 0.666 0.658 0.654 0.646 0.506 N 688,868 620,267 706,111 721,807 732,511 743,854 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Table 3 Average Yearly Premium for Women 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .061*** (.003) .052*** (.003) .061*** (.003) .055*** (.003) .055*** (.003) .017*** (.003) .050 Associates .125*** (.003) .116*** (.003) .118*** (.003) .109*** (.003) .112*** (.003) .113*** (.004) .116 Bachelors .334*** (.003) .320*** (.003) .333*** (.003) .327*** (.003) .329*** (.003) .327*** (.003) .328 Masters .513*** (.004) .503*** (.004) .515*** (.003) .506*** (.003) .507*** (.003) .496*** (.004) .507 Doctoral .661*** (.008) .669*** (.008) .679*** (.007) .680*** (.007) .674*** (.007) .681*** (.008) .674 Professional .645*** (.007) .634*** (.007) .653*** (.006) .662*** (.006) .675*** (.006) .637*** (.007) .651 Adj. R 2 0.698 0.697 0.690 0.686 0.680 0.524 N 669,982 603,668 682,889 697,172 706,971 714,625 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Figure 2 displays the coefficients under the average column in Tables 2 and 3 on a bar graph. It compares the wage returns for men (in blue) against women (in pink) and the average general returns (in gray) at each tertiary education level displayed on the horizontal axis. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. For most levels, the differences between the returns for men and woman are negligible and close to the national average. The returns to a bachelor’s degree are slightly higher for men. The largest difference lies in a professional degree, with men earning about 10% more on average than women. Studies on the income difference between male and female physicians discovered that female physicians earn 12–13% less than their male counterparts, even after controlling for personal characteristics, age, and specialty (Ohsfeldt & Culler, 1986 ). Studies on the market for lawyers have found that in the legal professions, women earn less than men and are underrepresented in high-paying senior ranks (Hersch, 2003 ). This may explain this paper’s results. McDonald and Thorton’s (2007) finding that the gender wage gap is primarily due to differences in college major choice cannot be disregarded, however Bobbitt-Zeher ( 2007 ) suggests that this is not the primary reason for the gap. Her research shows that controlling for standardized test scores, college major, and college selectivity, the wage gap at the bachelor’s level will still be about $ 4,400 per year. Factors such as family, employment, and marital status play a larger role. Additionally, the return for women with less than a year of college in 2019 is negative. This suggests that women work more weeks than the average high school graduate while earning less than that of a high school graduated person. Why this is the case is unknown. A possible explanation may be that these women are working in unpaid or low salary internships during their time in college. 4.3 Results on returns based on Race For the average returns for people of different race, a regression of the model (1) is run on White, Black and Asian Americans. Hispanics are not included in the estimations because they are coded differently in the Census, which makes comparisions difficult. The returns for White people are shown in Table 4 , the returns for Black people in Table 5 , and the returns for Asian people in Table 6 . Figure 3 graphically represents the returns between each race. Table 4 Average Yearly Premium for White Americans 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .063*** (.002) .065*** (.002) .072*** (.002) .068*** (.002) .070*** (.002) .025*** (.003) .061 Associates .124*** (.003) .118*** (.003) .119*** (.003) .111*** (.003) .112*** (.003) .118*** (.003) .117 Bachelors .356*** (.002) .349*** (.002) .359*** (.002) .356*** (.002) .360*** (.002) .356*** (.003) .356 Masters .522*** (.003) .523*** (.003) .528*** (.003) .523*** (.003) .524*** (.003) .505*** (.003) .521 Doctoral .681*** (.006) .693*** (.006) .708*** (.006) .703*** (.006) .693*** (.005) .691*** (.006) .695 Professional .709*** (.005) .722*** (.005) .730*** (.005) .729*** (.005) .739*** (.005) .700*** (.006) .722 Adj. R 2 0.688 0.687 0.681 0.677 0.669 0.527 N 1,100,861 991,831 1,119,613 1,142,992 1,162,665 1,180,079 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Table 5 Average Yearly Premium for Black Americans 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .064*** (.006) .058*** (.006) .045*** (.006) .054*** (.006) .052*** (.006) .023*** (.007) .049 Associates .114*** (.007) .103*** (.008) .090*** (.007) .095*** (.007) .086*** (.007) .101*** (.009) .098 Bachelors .304*** (.007) .295*** (.007) .294*** (.007) .301*** (.007) .286*** (.007) .307*** (.008) .298 Masters .463*** (.009) .451*** (.009) .454*** (.009) .435*** (.009) .455*** (.009) .436*** (.010) .449 Doctoral .635*** (.021) .558*** (.022) .594*** (.021) .596*** (.021) .611*** (.020) .602*** (.024) .599 Professional .611*** (.020) .625*** (.020) .600*** (.019) .607*** (.018) .640*** (.018) .589*** (.023) .612 Adj. R 2 0.667 0.662 0.650 0.646 0.645 0.460 N 139,316 121,863 141,685 141,888 141,215 139,837 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Table 6 Average Yearly Premium for Asian Americans 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .058*** (.010) .062*** (.010) .059*** (.010) .060*** (.010) .057*** (.010) .034*** (.011) .055 Associates .116*** (.012) .129*** (.012) .134*** (.012) .108*** (.011) .112*** (.011) .133*** (.013) .122 Bachelors .323*** (.009) .327*** (.009) .332*** (.009) .319*** (.009) .342*** (.009) .361*** (.010) .334 Masters .518*** (.011) .533*** (.011) .531*** (.010) .521*** (.010) .547*** (.010) .559*** (.012) .535 Doctoral .662*** (.016) .707*** (.016) .715*** (.015) .688*** (.014) .695*** (.014) .724*** (.016) .699 Professional .667*** (.016) .680*** (.016) .731*** (.015) .697*** (.015) .725*** (.015) .716*** (.017) .703 Adj. R 2 0.706 0.704 0.699 0.696 0.687 0.560 N 82,775 79,260 89,375 95,454 98,585 103,180 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Figure 3 compares the coefficients under the average return column in Tables 4, 5, and 6. The average general returns are shown as a comparison. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. While a neglible return difference exists between White and Asian people, with the returns for White people nearly mirroring the national average and Asians having a slightly lower return on average, the difference between these two groups and Black people is clearly observable. Black Americans earn about 6–7% less than White Americans for a bachelor’s and master’s degree, and nearly 10% less than White people for a professional and doctoral degree. This is consistent with previous research that find lower wages for Black individuals using the Bureau of Labor Statistics March 2013 Current Population Survey (Kamara, 2015 ). Other studies have also found that while the wage gap for Hispanic and Asian men are attributable primarily to educational and English proficiency differences, these factors only explain a quarter of the wage gap for Black men (Black et. al, 2006 ). The Black-White wage gap seems to increase with an increase in proportion of Black people in local populations (Beggs, Villemez, and Arnold, 1997). Huffman and Cohen ( 2004 ) found that this is because a greater Black population is postively correlated with higher exclusion of Black workers from higher-paying jobs. The return for Black Americans with less than a year of college in 2019 is negative, like that of the 2019 premium for women. This once again may be that Black Americans are working in unpaid or low salary internships during their time in college more and receive lower salaries than the average high school graduate. 4.4 Results on returns based on Citizenship For the average premium for people of different citizenship status in the U.S., model (1) is run on people of US-born citizenship, naturalized citizenship, and non-citizenship statuses. A non-citizen, according to the US Census Bureau, includes legal permanent residents, undocumented residents, temporary and humanitarian migrants, and temporary residents, and foreign students (US Census Bureau, 2006). The returns for US-born citizens are shown in Table 7 , the returns for naturalized citizens in Table 8 , and the returns for non-citizens in Table 9 . Figure 4 provides a graphical comparison of the returns for people of different citizenship status. Table 7 Average Yearly Premium for US-Born Citizens 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .065*** (.002) .065*** (.002) .068*** (.002) .066*** (.002) .066*** (.002) .026*** (.002) .059 Associates .125*** (.003) .115*** (.003) .117*** (.003) .110*** (.002) .110*** (.002) .120*** (.003) .116 Bachelors .358*** (.002) .348*** (.002) .361*** (.002) .356*** (.002) .362*** (.002) .363*** (.002) .358 Masters .521*** (.003) .521*** (.003) .525*** (.003) .519*** (.003) .521*** (.003) .505*** (.003) .519 Doctoral .673*** (.006) .680*** (.006) .700*** (.006) .687*** (.006) .680*** (.006) .678*** (.007) .683 Professional .717*** (.005) .728*** (.005) .736*** (.005) .734*** (.005) .744*** (.005) .715*** (.006) .729 Adj. R 2 0.692 0.690 0.684 0.680 0.674 0.527 N 1,178,825 1,056,981 1,199,615 1,221,019 1,238,262 1,254,985 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Table 8 Average Yearly Premium for Naturalized Citizens 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .062*** (.008) .068*** (.008) .094*** (.008) .061*** (.002) .059*** (.002) .033*** (.009) .063 Associates .108*** (.009) .109*** (.009) .109*** (.009) .093*** (.009) .097*** (.009) .063*** (.010) .097 Bachelors .283*** (.008) .292*** (.008) .293*** (.008) .281*** (.007) .269*** (.007) .259*** (.008) .280 Masters .460*** (.009) .472*** (.010) .484*** (.009) .476*** (.009) .480*** (.009) .457*** (.010) .472 Doctoral .714*** (.015) .716*** (.015) .737*** (.014) .703*** (.014) .698*** (.014) .710*** (.015) .713 Professional .674*** (.014) .675*** (.015) .729*** (.014) .686*** (.014) .706*** (.013) .662*** (.015) .689 Adj. R 2 0.615 0.615 0.605 0.605 0.599 0.468 N 93,754 89,094 100,118 104,683 107,766 110,785 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Table 9 Average Yearly Premium for Non-citizens 2014 2015 2016 2017 2018 2019 Average College 1 year. no degree .018*** (.009) .016*** (.010) .028*** (.009) .031*** (.009) .035*** (.010) .007*** (.012) .023 Associates .075*** (.012) .089*** (.013) .072*** (.013) .070*** (.012) .061*** (.012) .054*** (.015) .070 Bachelors .242*** (.009) .247*** (.010) .240*** (.009) .244*** (.009) .249*** (.009) .185*** (.011) .235 Masters .439*** (.012) .450*** (.012) .451*** (.011) .447*** (.011) .462*** (.011) .423*** (.013) .445 Doctoral .575*** (.018) .612*** (.018) .624*** (.017) .653*** (.017) .621*** (.017) .593*** (.020) .613 Professional .444** (.021) .444** (.022) .415** (.020) .437** (.020) .463** (.020) .386** (.024) .432 Adj. R 2 0.661 0.664 0.657 0.650 0.645 0.476 N 66,160 59,433 68,014 71,160 70,778 70,025 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Figure 4 compares the returns for US-born citizens (in red) with naturalized citizens (in blue) and non-citizens (in yellow). It displays the coefficients under the average column in Tables 7, 8, and 9 on a bar graph. The average general returns are shown as a comparison. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. The returns for citizenship status generally show a downwards trend from a US-born citizen to a non-citizen, with US-born citizens earning about the national average for each level of tertiary education, naturalized citizens earning slightly less, and non-citizens earning significantly less, except at the doctoral level. A non-citizen earns 12% less for a bachelor’s degree and nearly 30% less for a professional degree than a US citizen. Sumption and Flamm’s ( 2012 ) research arrived at a similar conclusion. This result is likely because non-citizens face work restrictions. U.S. immigration laws purposefully make it difficult for non-citizens to obtain work in the United States to protect the opportunities for U.S. workers (Ingraham, 2011 ). The federal government limits the ways non-citizens can obtain work authorization in the U.S. and sets time limits on most authorized work. Employers may need to sponsor noncitizens for work visas, which requires paperwork with the USCIS and filing fees, further adding to the costs and barriers for non-citizens to obtain work sponsorship. Furthermore, non-citizens have limited opportunity in the labor market since their visa status is tied to their employer, making it difficult to compete across different vacancies. Additionally, the average yearly premium for noncitizens with less than a year of college in 2019 is negative. This suggests once again that these individuals work more weeks than the average high school graduate while earning less than that of a high school graduated person. 4.5 Results on returns based on Occupation For the average returns to different occupation types, a regression of model (1) is run on people in management, business, healthcare, legal, computer, and engineering occupations. Under business occupations includes finance occupations, under engineering occupations includes architecture occupations, and under computer occupations also includes math occupations. The returns for these occupations are shown in Table 10 . Figure 5 graphically compares the returns for each occupation against the general returns. Table 10 Average Yearly Premium for Different Occupations 2014 2015 2016 2017 2018 2019 Average Management Occupations College 1 year. no degree .156*** (.008) .140*** (.008) .147*** (.007) .150*** (.007) .133*** (.008) .140** (.008) .144 Associates .186*** (.009) .158*** (.009) .146*** (.009) .143*** (.009) .128*** (.009) .143*** (.009) .151 Bachelors .513*** (.007) .500*** (.007) .514*** (.006) .508*** (.006) .496*** (.006) .510*** (.007) .507 Masters .688*** (.007) .679*** (.008) .682*** (.007) .680*** (.007) .676*** (.007) .695*** (.008) .683 Doctoral .794*** (.014) .794*** (.015) .797*** (.014) .789*** (.014) .778*** (.014) .810*** (.015) .794 Professional .765*** (.015) .761*** (.015) .759*** (.014) .745*** (.014) .774*** (.014) .750*** (.015) .759 Adj. R 2 0.478 0.478 0.464 0.462 0.458 0.352 N 142,716 132,048 152,029 157,916 154,228 166,704 Business Occupations College 1 year. no degree .088*** (.012) .089*** (.013) .099*** (.012) .073*** (.0012) .089*** (.009) .066*** (.010) .084 Associates .068*** (.013) .076*** (.013) .072*** (.013) .082*** (.013) .079*** (.010) .088*** (.011) .078 Bachelors .389*** (.010) .385*** (.011) .401*** (.010) .403*** (.010) .422*** (.008) .429*** (.008) .405 Masters .550*** (.011) .564*** (.012) .573*** (.011) . 568*** (.011) .600*** (.008) .608*** (.009) .577 Doctoral .605*** (.028) .699*** (.028) .648*** (.027) .703*** (.026) .715*** (.018) .717*** (.020) .681 Professional .626*** (.020) .635 *** (.021) .631*** (.020) .661*** (.020) .669*** (.015) .680*** (.018) .650 Adj. R 2 0.560 0.562 0.545 0.539 0.509 0.376 N 70,727 64,208 73,784 76,618 124,597 128,895 Health Occupations College 1 year. no degree .162*** (.011) .150*** (.002) .175*** (.012) .014*** (.012) .134*** (.011) .129*** (.014) .127 Associates .405*** (.011) .386*** (.011) .409*** (.011) .370*** (.011) .374*** (.010) .374*** (.013) .386 Bachelors .578*** (.010) .565*** (.011) .578*** (.010) .560*** (.010) .576*** (.010) .577*** (.012) .572 Masters .696*** (.012) .700*** (.013) .735*** (.012) .698*** (.011) .709*** (.011) .700*** (.014) .706 Doctoral .972*** (.014) .977*** (.015) 1.041*** (.014) .971*** (.014) .984*** (.014) .978*** (.016) .987 Professional 1.098*** (.012) 1.129*** (.013) 1.164*** (.012) 1.105*** (.012) 1.146*** (.011) 1.124*** (.014) 1.128 Adj. R 2 0.584 0.579 0.577 0.569 0.568 0.421 N 87,890 79,417 91,062 94,854 97,368 99,195 Legal Occupations College 1 year. no degree .012*** (.020) .026*** (.021) .028*** (.020) .025*** (.020) .037*** (.018) − .050*** (.021) .013 Associates .133*** (.022) .107*** (.024) .105*** (.022) .079*** (.022) .112*** (.021) .078*** (.024) .102 Bachelors .286*** (.017) .275*** (.019) .275*** (.017) .278*** (.017) .285*** (.016) .256*** (.019) .276 Masters .434*** (.017) .427*** (.019) .442*** (.017) .433*** (.017) .437*** (.016) .398*** (.019) .429 Doctoral .484*** (.028) .416*** (.029) .455*** (.028) 435*** (.028) .487*** (.026) .414*** (.031) .449 Professional .371*** (.028) .369*** (.030) .390*** (.028) .393*** (.027) .396*** (.027) .294*** (.032) .369 Adj. R 2 N 0.602 26,206 0.603 24,050 0.599 27,049 0.590 28,069 0.617 31,033 0.446 33,136 Computer Occupations College 1 year. no degree .110*** (.015) .041*** (.016) .090*** (.015) .064*** (.014) .091*** (.014) .086*** (.016) .080 Associates .099*** (.016) .040*** (.017) .085*** (.016) .067*** (.015) .083*** (.015) .165*** (.017) .090 Bachelors .383*** (.014) .313*** (.014) .378*** (.014) .368*** (.013) .401*** (.013) .482*** (.014) .388 Masters .502*** (.015) .429*** (.015) .500*** (.015) .487*** (.014) .530*** (.014) .620*** (.016) .511 Doctoral .636*** (.024) .555*** (.025) .643*** (.023) .658*** (.022) .711*** (.022) .829*** (.025) .672 Professional .447*** (.033) .340*** (.035) .466*** (.031) .492*** (.030) .529*** (.030) .563*** (.035) .473 Adj. R 2 0.600 0.579 0.593 0.577 0.581 0.422 N 40,407 37,990 44,688 47,111 49,985 53,431 Engineering Occupations College 1 year. no degree .059*** (.015) .131*** (.018) .090*** (.017) .083*** (.017) .133*** (.016) .060*** (.002) .093 Associates .117*** (.017) .150*** (.018) .125*** (.017) .113*** (.017) .154*** (.016) .173*** (.019) .139 Bachelors .461*** (.014) .483*** (.016) .473*** (.015) .460*** (.014) .513*** (.014) .559*** (.016) .492 Masters .613*** (.016) .624*** (.017) . 598*** (.016) .596*** (.015) .649*** (.015) .715*** (.017) .633 Doctoral .736*** (.025) .756*** (.026) .797*** (.025) .756*** (.024) .827*** (.023) .849*** (.026) .787 Professional .424*** (.034) .551*** (.034) .540*** (.032) . 435*** (.032) .539*** (.031) .599*** (.035) .515 Adj. R 2 0.593 0.590 0.590 0.585 0.577 0.421 N 27,247 24,466 28,715 29,682 30,561 34,719 Notes : Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped. Figure 5 compares the returns for management, business, health, law, computer, and engineering occupational returns against the average general returns. It displays the coefficients under the average column in Table 10 for each occupation and level of education. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. Generally, at each level of higher education, Healthcare professions have the greatest wage increase across all levels followed by Management professions. Law professions seem to see the lowest wage increase compared to the other occupations at every level. For a college education with no degree, the occupations with the highest return (average of 14%) are Healthcare and Management. Law occupations receive the lowest wage increase, at a 2% increase from someone with a high school diploma. For a bachelor’s degree, master’s, professional and doctoral degree, the trend of Healthcare and Management professions seeing the highest returns whereas Law sees the lowest holds. Returns for Healthcare peak at the professional level, at nearly 115% wage increase relative to someone with just a high school diploma. The results we find are consistent with previous literature suggesting that returns will vary depending on the occupation (Oreopoulos and Petronijevic, 2013 ). Ketel et. al. (2013) studied the returns to a medical school in the Netherlands and determined that every year post graduation, doctors earn at least 20% more than people who end up in their next-best occupation. Simkovic and McIntyre ( 2014 ) used both a present value and annual earning calculation to estimate the economic value of a law degree. They found that a law degree has mean annual earnings premium of approximately $ 57,200 in 2013 dollars, and the mean lifetime value of a law degree is approximately $ 1 million. These numbers, however, don’t seem align with our findings. For our regression, the legal returns are significantly lower than that of the other jobs and the national average. This may be because people in legal occupations earn less than what is expected. Another hypothesis is that there are so many law graduates that the supply drives the wage premium down. The lower return at the professional compared to the master’s and doctoral education level is also confusing given that the juris doctor degree is a professional degree and the degree someone needs in order to practice law. These differences, however, are likely due to the nature of the dataset and how the Census categorizes reported occupations. Partners at law firms are the highest paid lawyers and may be categorized under management occupations because their duties more closely parallel managing tasks (managing the firm, establishing financial and operational strategies, etc.) despite being lawyers 7 . This, however, may not explain all the counterintuitive trends in the results. 4.6 Conclusion To conclude, regardless of sex, race, and citizenship status, one can expect to earn higher annual financial returns on average with higher levels of education. Significant wage increases occur once someone obtains a bachelor’s degree or higher. However, there are variations in the benefits to people of different race and citizenship status. The gender wage gap, as suggested by existing literature, is relatively small but exists at the professional level. There is a significant premium differential between Black and White/Asian Americans, and between citizens and non-citizens, even when controlling for occupation and education differences. Different occupations also have varying returns to different levels of tertiary education. This suggests that occupational choice plays a huge role in wage determination. Although existing literature gives us some direction as to why these trends exist, more research needs to be done to conclusively determine the underlying causes of the observed wage gaps. Specifically, further research should be pursued on determining the cause of the gender wage gap, the racial gap, and the wage premium to US citizenship. Furthermore, gaps between existing findings and our own, such as the returns to a college degree, need explanations. Declarations Author Contribution This manuscript is written under the guidance of AO by EL. Acknowledgement No one else contributed towards writing of this manuscript. No funding was received in preparing this manuscript. References Altonji, J. G., & Zimmerman, S. D. (2017). The costs of and net returns to college major (No. w23029). National Bureau of Economic Research. Barro, R. J., & Lee, J. W. (2013). A new data set of educational attainment in the world, 1950–2010. Journal of development economics, 104, 184-198. Beggs, John J., Wayne J. Villemez, and Ruth Arnold. (1997). Black Population Concentration and Black-White Inequality: Expanding the Consideration of Place and Space Effects. Social Forces, 76, 65–91. Black, D., Haviland, A., Sanders, S., & Taylor, L. (2006). Why do minority men earn less? A study of wage differentials among the highly educated. The Review of Economics and Statistics, 88 (2), 300-313. Boarini, R., & Strauss, H. (2010). What is the private return to tertiary education? New evidence from 21 OECD countries. OECD Journal: Economic Studies, 2010. Bobbitt-Zeher, D. (2007). The Gender Income Gap and the Role of Education. Sociology of Education, 80, 1–22. Carnevale, A. P., Cheah, B., & Rose, S. J. (2011). The College Payoff: Education, Occupations, Lifetime Earnings, report prepared for the Center on Education and the Workforce. Dale, S. B., & Krueger, A. B. (2002). Estimating the payoff to attending a more selective college: An application of selection on observables and unobservables. The Quarterly Journal of Economics, 117(4), 1491-1527. Goldin, C. (2004.) The Long Road to the Fast Track: Career and Family. Annals of the American Academy of Political and Social Science, 596(1), 20–35. Goldin, C. (2006.) The Quiet Revolution That Transformed Women’s Employment, Education, and Family. American Economic Review, 96(2), 1–21. Hersch, J. (2003). The New Labor Markets for Lawyers: Will Female Lawyers Still Earn Less. Cardozo Women's LJ, 10, 1. Hout, M. (2012). Social and economic returns to college education in the United States . Annual review of sociology, 38, 379-400. Huffman, M., & Cohen P. (2004). Racial Wage Inequality: Job segregation and Devaluation across U.S. Labor Markets. AJS , 109(4), 902–36. Ingraham, M. (2011). Citizenship Guide: Hiring Non-Citizens. Bernard Koteen Office of Public Interest Advising, Harvard Law School Kamara, J. (2015). Decomposing the Wage Gap: Analysis of the Wage Gap Between Racial and Ethnic Minorities and Whites. Pepperdine Policy Review , 8(1). Ketel, N., Leuven, E., Oosterbeek, H., & van der Klaauw, B. (2016). The returns to medical school: Evidence from admission lotteries. American Economic Journal: Applied Economics , 8(2), 225-54. Simkovic, M., & McIntyre, F. (2014). The economic value of a law degree. The Journal of Legal Studies, 43(2), 249-289. Maurin E., McNally S. (2008.) Vive la revolution! long-term educational returns of 1968 to the angry students. Journal of Labor Economics, 26, 1-33. McDonald, J. A., & Thornton, R. J. (2007). Do new male and female college graduates receive unequal pay? Journal of Human Resources, 42(1), 32-48. Ohsfeldt, R. L., & Culler, S. D. (1986). Differences in income between male and female physicians. Journal of health economics, 5 (4), 335-346. Oreopoulos, P., & Petronijevic, U. (2013). Making college worth it: A review of research on the returns to higher education (No. w19053). National Bureau of Economic Research. Page, M. E. (2010). Signaling in the labor market. Economics of education. Oxford: Elsevier. Schultz, T. (1961). Investment in Human Capital. The American Economic Review, 51(1), 1-17. Spence, A. M. (1973). Job market signaling. Quarterly Journal of Economics, 87, 355-374. Strauss, H., & de la Maisonneuve, C. (2007). The wage premium on tertiary education. OECD Journal: Economic Studies, 2009. Sumption, M., & Flamm, S. (2012). The economic value of citizenship for immigrants in the United States. Washington, DC: Migration Policy Institute. U.S. Department of Education, National Center for Education Statistics. (2004.) 2003–04 National Postsecondary Student Aid Study. Retrieved from nces.ed.gov/surveys/npsas. Weiss, A. (1995.) Human Capital vs. Signalling Explanations of Wages. Journal of Economic Perspectives, 9(4), 133–154. Footnotes [3] The occupations examined in this paper are management occupations, business and finance occupations, health occupations, legal occupations, computer and math occupations, and engineering and architecture occupations as classified by the US Census Bureau. [4] Business cycle state and other cohort variations are not factored out of these numbers. [5] We ran the 2018 regression without weeks worked as a control and obtained nearly identical numbers to the current 2019 returns [6] Other studies typically use a present-value calculation, and the calculated returns are for a life cycle of earnings [7] According to the subject matter experts at the US Census Bureau, occupations are coded based on what the respondent describes as their most important duties. Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Eryn","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-06-10 23:08:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6866499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6866499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85659910,"identity":"92e49c56-b32a-4be4-b38d-d34f4a3f0ed1","added_by":"auto","created_at":"2025-06-30 11:26:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph of the Returns to Each Level of Tertiary Education\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6866499/v1/b7a72fdbd004743f1d4415bc.png"},{"id":85660249,"identity":"af7b7053-59bd-438c-9115-234a62a61ec7","added_by":"auto","created_at":"2025-06-30 11:34:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph Comparing Returns for Men and Women\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6866499/v1/db7eadae9f7092aafd2a36b9.png"},{"id":85659911,"identity":"4801313c-7abc-4043-9cec-cfccd1876170","added_by":"auto","created_at":"2025-06-30 11:26:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph Comparing Returns for People of Different Race\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6866499/v1/bf51fdca7de9928fdcd8f929.png"},{"id":85659914,"identity":"9e72a591-082b-40e1-b8a6-59df912225a5","added_by":"auto","created_at":"2025-06-30 11:26:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph Comparing Returns for People of Citizenship Status\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6866499/v1/bae3670145c9f732ba78036d.png"},{"id":85659920,"identity":"3cbf6c0e-8711-4742-980b-3b0a7f9c69f6","added_by":"auto","created_at":"2025-06-30 11:27:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph Comparing Returns for People of Occupations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6866499/v1/840d266b84feee775a401d0a.png"},{"id":85661231,"identity":"97880492-a717-4e4c-b013-8232e0c9e850","added_by":"auto","created_at":"2025-06-30 11:51:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1948431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6866499/v1/32392d04-f0d8-42a1-845f-784870ac9782.pdf"},{"id":85661100,"identity":"2895693c-688c-437c-ab7e-5016d4e8f2fc","added_by":"auto","created_at":"2025-06-30 11:42:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18275,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6866499/v1/042fabb27d60d7d503ace91f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Returns to Tertiary Education and the Differences by Gender, Race, Citizenship, and Occupation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe decision to invest in higher education has always been an important one in the United States, where many people opt for a bachelor\u0026rsquo;s degree and beyond. Given the high costs of higher education, it is important for people to understand the economic returns to the different levels of tertiary education. In this paper we quantify the average yearly premium to the increased income margin of tertiary education for years 2014 to 2019 using the Mincer equation. The premium stated for each level is the increased yearly income margin compared to the average return for someone with no tertiary education. The levels include less than a year of college with no degree, some years of college with no degree, an associate, bachelor\u0026rsquo;s, master\u0026rsquo;s, doctoral, and professional degree. In addition to the financial reward for each degree type, this paper will further examine the wage gap between men and women, people of different races, citizenship statuses, and people in different occupations\u003csup\u003e3\u003c/sup\u003e. Unlike studies that calculate a present value for the return to higher education, this paper only provides the average yearly reward; the cost of education is not accounted for, and income growth rates are not calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile there are countless articles that examine returns to an additional year of schooling, fewer focus on higher degrees, and few comprehensively compare the earning potential at each level of tertiary education. There are also few papers that comprehensively compare the returns to people under several different social categories within a single study. Our goal is to estimate how much someone can expect to earn on average annually above a high school diploma, controlling for their characteristics and tertiary educational attainment. This estimation informs someone of the average annual value of their income earnings and allows someone to estimate this value irrespective of their life expectancy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are two predominant theories in literature explaining the increase in income as years of education increases: the signaling theory and the human capital theory. Both examine the relationship between the years of education and subsequent earnings potential, but the two have different theoretical explanations for the increased earning potential. However, both imply a positive causal relationship between education and income.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe human capital theory is widely regarded to originate from Theodore Schultz. This theory explains the education-wage relationship as a causal one; as someone becomes more educated, they increase their productivity and skills (Schultz, 1961). Employers then reward these more productive individuals with higher wages in the market.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe signaling theory is usually attributed to Michael Spence. Contrary to the human capital theory, the signaling model claims that educational attainment is a signal of a worker\u0026rsquo;s innate productivity and not an enhancer (Spence, 1973). Page (2010) states the following assumptions for the theory: (1) workers have differing innate levels of ability not affected by education; (2) higher educational attainment incurs higher costs, including psychological costs, which are higher for those of low productivity and lower for those of high productivity; (3) individual workers know their true level of productivity and employers do not; (4) schooling levels can be observed at no cost. Because employers cannot directly observe a worker\u0026rsquo;s innate ability, educational attainment becomes the signal that employers use to estimate a worker\u0026rsquo;s true ability. Worker productivity must be negatively related to the cost of acquiring the education signal, and the cost differences that arise between high and low ability workers must arise from differences in cognitive ability alone, and not from outside influences such as differences in financial situations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the signaling model may include different assumptions, it does not exclude the possibly for human capital accruement (although human capital models require it). Both theories assume that people must behave rationally and invest in education as long as the marginal benefit of an additional year of schooling is greater than the marginal cost. Weiss (1995) hypothesizes that the two theories may not be mutually exclusive. There have been several studies testing the plausibility of the competing theories, but few studies have convincingly argued for either. This may be because different educational pathways carry one effect more than the other. For example, a technical pathway, such as engineering, may experience a greater human capital effect since their education often involves developing skills that are directly applied in its particular field. On the other hand, a liberal arts education does not specialize in technical skills but rather aims to develop a broader spectrum of knowledge; the skills and accomplishments demonstrated here signal future success. Thus, comparing outcomes for people in liberal arts pathways versus those in engineering or technical pathways may help clarify the differences between the two theories. In sum, these theories suggest differing effects of education on ability, but both imply a positive causal relationship between education and income.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn existing literature, this positive correlation between schooling and earnings potential exists as well. Barro (2010) found that an additional year of schooling equates to a 5% to 12% increase in earnings, which is consistent with most Mincerian estimates in literature. Goldin (2004, 2006) discovered a positive correlation between income and college education by examining the narrowing gender wage gap in early 1980 when women increased their college attendance and entering of the labor force. This paper will estimate this relationship between schooling and earnings potential between men and women, between White, Black, and Asian Americans, between US-born, naturalized, and non-citizens, and between management, business, health, legal, computer, and engineering occupations.\u003c/p\u003e\n\u003cp\u003eThis paper\u0026rsquo;s data is from the American Community Survey, a demographics survey conducted by the US Census Bureau. All persons in the dataset with an educational attainment at and above high school for the years 2014 to 2019 were used for our analysis. We use the Mincerian approach of explaining wages by regressing the logarithm of yearly wages of each individual on a vector of all tertiary educational attainment levels and a vector of control variables such as age, sex, race, and more (see Appendix 1) to estimate the quantified returns to higher education. We compare the average yearly income margin of different levels of tertiary education to that of a person who completed only secondary education, which is a high school diploma.\u003c/p\u003e\n\u003cp\u003eOur results show that earnings potential increases with a higher level of tertiary education. The yearly earnings premium for the different levels of tertiary education, each compared to the earnings of someone with no tertiary education, are as follows: less than a year of college without a degree: 3.8%; some years of college without a degree: 5.8%; associate: 11.3%; bachelor\u0026rsquo;s: 34.6%; master\u0026rsquo;s: 51.5%; doctoral: 68.6%; and professional: 71.3%. For an associate, master\u0026rsquo;s and doctoral degree, the yearly premium is nearly equal between men and women. However, women earn 10% less than men at the professional level. Across all levels, Black people earn less than White and Asian people. The gap widens at higher degrees. Non-citizens also consistently earn less than citizens at each level. This, however, may be due to visa work restrictions and language barriers. Occupational differences are relatively consistent with other findings, with the exception that all occupations except healthcare workers earn more at the doctoral than the professional level. \u0026nbsp;\u003c/p\u003e"},{"header":"2. Related Literature","content":"\u003cp\u003e This paper follows the extensive literature on the topic of returns to higher education. Many studies have examined the returns to a college degree. Most researchers, such as Carnevale et al ( \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e ), use a present value calculation. He found that an adult in 2009 with a bachelor\u0026rsquo;s degree earned 84% more over a lifetime than a high school graduate did. Hout ( \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e ) used the US Census Bureau data and calculated that in 2002, within a forty-year work life, men with college degrees can expect to earn \u003cspan\u003e$\u003c/span\u003e2,955,000 over their lifetime as opposed to \u003cspan\u003e$\u003c/span\u003e1,664,000 with a high school diploma; the college degree can earn its costs several times over. This paper chooses to look at annual returns as opposed to a lifetime of earnings because it is not well-studied within literature, and this paper\u0026rsquo;s goal is to quantify the wage/salary premium at each level of tertiary education. The high return to tertiary education exists in cross-country studies as well. Boarini and Strauss ( \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e ) found that from 1991 to 2005, the returns to an additional year of tertiary education are on average 8% and range from 4\u0026ndash;15% depending on the country. Studies have also found that large wage premia differences across countries exist. The gross wage premium is 27% for Spanish men and nearly 90% for Hungarian and US degree holders. The per annum premium is 17% for men and women in Hungary and the United States (Strauss \u0026amp; Maisonneuve, 2009). \u003c/p\u003e \u003cp\u003eAlthough the returns to higher education are positive, they differ between different people. Maurin and McNally (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found the largest returns to college were for those who would have been rejected but were instead accepted to a school; the average student\u0026rsquo;s returns are lower compared to marginal students. This is not to say, however, that the returns for the average student are not positive.\u003c/p\u003e \u003cp\u003eMany studies have also studied whether an elite or high-quality college has a higher premium than an average school. Evidence of a higher wage reward from elite colleges have been mixed. Dale and Krueger (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) compared college freshmen who were admitted to a selective university to those who were admitted to a less selective one and the elite college but enrolled at the less selective university. The students in the latter group earned as much as those enrolled at the elite school. Other studies have found a statistically significant difference in premium from enrolling at an elite school. Hoekstra (2009) studied students enrolled in a selective state university just above the admissions cutoff and students right below the cutoff who attended a less selective university. They found that attending the most selective state university increased earnings 20% for white males.\u003c/p\u003e \u003cp\u003eThe gender wage gap has been less studied in recent years due to evidence of its closing. McDonald and Thornton (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) studied this gap by selecting college graduates newly entering the workforce to control for educational levels and job experience. They found that in 2006, women earned about 90% of what men earned, but 90% of this gap could be explained by differences in major choices.\u003c/p\u003e \u003cp\u003eThe racial wage gap, on the other hand, still exists. Many studies have found evidence of this phenomenon. Studies conducted on wage differentials between White, Black and Hispanic people have found that overall, without any controls, White people earn about 5% and 6% more than Black and Hispanic people, respectively. However, even after adding controls such as education and citizenship, unexplained differences still existed, which suggested systemic racial discrimination, especially towards Black individuals (Kamara, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFewer studies have examined returns for individuals of different citizenship status. Sumption and Flamm (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found that naturalized citizens tend to earn more than noncitizens for reasons such as higher education, better English ability, lower unemployment chance, and more work experience. However, even when these factors are controlled for, naturalized citizens are found to still earn at last 5% more than noncitizens, which suggests there is a citizenship premium. Furthermore, immigrants pursuing graduate degrees are slightly more likely to be in the lowest income quintile than the average student. Graduate immigrant students are also more likely to have dependents, which adds additional financial pressure (NCES, 2004).\u003c/p\u003e \u003cp\u003eOccupation and higher education are highly related. As someone obtains schooling beyond a bachelors, they increasingly specialize and select themselves into a particular work industry. Hence, it is important to examine the returns for different occupations. Oreopoulos and Petronijevic (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) discovered that the wage premium to tertiary education is higher than that of high school but varies significantly across different occupations; returns depend on education \u003cem\u003eand\u003c/em\u003e occupational choices. This implies that college major choice is an important consideration for future returns. Carnevale et al (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found lifetime earnings to a bachelor\u0026rsquo;s degree are highest in the managerial, health professional, and science, technology, engineering, and mathematics occupations. Altonji and Zimmerman (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) had similar findings when examining the estimated net returns of students in different fields of study at Florida public universities. They found that the industries with the highest net returns were engineering, computer science, business, and health.\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp; Dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this paper is a subset of the American Community Survey, a yearly individual and household census conducted by the US Census Bureau. The publicized data is a random sample of 1% of the US population from the years 2005 to 2019. A person surveyed once may not surveyed again for the next upcoming five surveys.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor this study, all persons in the dataset with an educational attainment above high school diploma for the years 2014 to 2019 were used for analysis. This averages about 1.3 million individuals per year. The independent variable in this study is educational attainment level and the wage/salary income in the last annual year is used to measure the returns\u003csup\u003e4\u003c/sup\u003e. The educational levels examined in this study are some college education but less than one year of college, one or more years of college credit with no degree, an Associate degree (e.g., AA, AS), Bachelor\u0026rsquo;s degree (e.g., BA. BS), Master\u0026rsquo;s degree (e.g., MA, MS, MEng, MEd, MSW, MBA), Doctorate degree (e.g., PhD, EdD) and Professional degree beyond bachelor\u0026rsquo;s degree (e.g., MD, DDS, DVM, LLB, JD). The educational level assigned to each individual is their highest level obtained. For example, a person who completed a bachelor\u0026rsquo;s degree and a MD would be reported as having a professional degree.\u003c/p\u003e\n\u003cp\u003eThe variables sex, race, citizenship status, and occupation are used to measure the different outcomes of tertiary education on these groups. Specifically, this paper examines the wage gap between men and women, the average yearly premium difference between White, Black and Asian Americans, the average yearly return difference between US-born citizens, naturalized citizens, and noncitizens, and the return difference between people in management, business, health, legal, computer, and engineering occupations. Additional controls such as a person\u0026rsquo;s age, age\u003csup\u003e2\u003c/sup\u003e to account for experience, marital status, same sex households, English ability, disability status, annual weeks and hours worked, class of worker, family income, and quarter of birth are included to account for factors apart from education that may affect income. See Appendix 1 for the full variable list and descriptions.\u003c/p\u003e\n\u003cp\u003eHispanics account for a significant proportion of the US population at 18% of the total population. They are not included in this estimation because the US Census Bureau codes Hispanics differently than the other race categories. This makes comparing across race groups complicated. However, this is not to underestimate the importance of Hispanics in the US.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u0026nbsp; Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo measure the financial reward to each level of tertiary education, this paper will use the Mincerian estimation by regressing the logarithm of yearly wages on a vector of the levels of tertiary education. The following model is used to estimate the relationship between educational attainment on income:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Y = \u0026beta;o + X\u0026beta; + W\u003c/strong\u003e\u003cstrong\u003e\u0026Gamma;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;+ \u0026epsilon; (1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere Y is the logarithm of yearly wages/salary income per individual, \u0026beta;o is the constant, X is a vector of the levels of tertiary education, and \u0026beta; is a vector of the coefficients of the levels of education, which represents the percent increase in wage compared to someone with just a high school diploma. W is a vector of the control variables, \u0026Gamma; is a vector of the coefficients of the control variables, and \u0026epsilon; is the error term. Model (1) captures the averaged effect of each obtained level of tertiary education on wage returns for everyone who has an education beyond high school. Since the wage variable from the database has an upper limit of $999,999 earned annually, upper outliers are excluded from the analysis. The same model is used to compare returns between different genders, races, citizenship statuses, and occupations. In each case, the model will only be run on individuals that fall under each category. For example, for females, equation (1) is estimated by dropping gender as a control variable and is estimated for females only.\u0026nbsp;\u003c/p\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Results on general returns to tertiary education\u003c/h2\u003e \u003cp\u003eFor the annual average returns of different levels of tertiary education from 2014 to 2019, a regression of model (1) is run on individuals with an educational attainment above high school in these years. The coefficients displayed at each level represents the percent increase in wage from the average individual with no college education. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A graphical comparison of the returns is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium of Tertiary Education\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2014\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2015\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2016\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2017\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.045***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.042***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.042***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.039***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.041***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.017***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.063***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.063***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.067***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.065***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.065***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.026***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.122***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.114***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.115***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.107***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.107***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.114***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.347***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.339***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.349***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.345***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.349***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.347***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.515***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.516***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.520***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.515***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.519***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.502***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.673***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.684***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.700***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.691***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.685***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.685***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.701***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.711***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.723***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.718***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.731***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.694***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizenship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSame sex household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnglish ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass of worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeeks worked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,358,850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,223,935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,389,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,418,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,439,482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,458,479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. The variables weeks worked and same sex household were no longer in the 2019 ACS dataset and dropped from the 2019 regression.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 1 displays the coefficients under the average column in Table 1 on a bar graph. The levels of tertiary education are displayed on the horizontal axis and shows the increase in wage margin relative to someone with just a high school diploma on the vertical axis.\u0026nbsp;\u003c/p\u003e \u003cp\u003eAverage values in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represent the average returns across the six years for the given level of education. Conducting a pooled regression across all six years will provide similar results as the average values. The wage premium increases with higher tertiary educational attainment. The returns are consistent across the years with the exception of college with no degree in 2019. This is because the weeks worked control variable was not recorded in 2019 and not because of any actual changes in the returns\u003csup\u003e5\u003c/sup\u003eThe returns to a professional \u003e. These results suggest that weeks worked matter in precisely estimating the returns because some people work overtime without vacations. Because returns to college with no degree fell after weeks worked was dropped, it suggests that people without a college degree but with some amount of college education tend to work more weeks compared to other people. This is why dropping the variable has the largest effect for this group.\u003c/p\u003e \u003cp\u003eThe results from existing literature tend to support higher returns than our results (Carnevale et. al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Boarini \u0026amp; Strauss, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) but this may be because they are estimating a lifecycle of earnings, which account for income growth rates\u003csup\u003e6\u003c/sup\u003e. The returns to a professional degree may actually be higher than what is observed here because other monetary and nonmonetary benefits are not included in yearly salaries. These include bonuses, private health insurance, and other working benefits that are often provided in occupations that require a professional degree.\u003c/p\u003e \u003cp\u003eBy comparing the returns to a college education greater than one year but with no degree to that of a bachelor\u0026rsquo;s degree, we find evidence of the signaling theory. The return to a bachelor\u0026rsquo;s degree (.347) is more than four times than that of more than a year of college with no degree (.058). A college education may be creating human capital, but if the human capital effect was the one in effect, we should observe someone with a bachelor\u0026rsquo;s degree earns more than four times the wage of a person with more than a year of college education. This suggestive of signaling theory as the bachelor's diploma is used as a signal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results on returns based on Gender\u003c/h2\u003e \u003cp\u003eFor the average returns for men from 2014 to 2019, a regression of model (1) is run on all men in the dataset. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These steps are repeated for all women in the dataset to find their average returns from these years, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a graphical comparison between the returns for each sex against the general returns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium for Men\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.034***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.038***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.034***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.035***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.037***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.003***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.055***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.063***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.063***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.063***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.064***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.024***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.108***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.101***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.102***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.094***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.092***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.103***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.340***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.339***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.347***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.345***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.351***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.351***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.489***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.504***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.500***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.498***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.508***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.485***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.675***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.692***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.705***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.689***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.675***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.667***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.735***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.767***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.768***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.755***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.766***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.743***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e620,267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e706,111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e721,807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e732,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e743,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium for Women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.049***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.038***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.042***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.036***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.038***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.008***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.061***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.052***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.061***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.055***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.055***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.017***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.125***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.116***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.118***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.109***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.112***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.113***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.334***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.320***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.333***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.327***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.329***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.327***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.513***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.503***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.515***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.506***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.507***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.496***\u003c/p\u003e \u003cp\u003e(.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.661***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.669***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.679***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.680***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.674***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.681***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.645***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.634***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.653***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.662***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.675***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.637***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e669,982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e603,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e682,889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e697,172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e706,971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e714,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 2 displays the coefficients under the average column in Tables 2 and 3 on a bar graph. It compares the wage returns for men (in blue) against women (in pink) and the average general returns (in gray) at each tertiary education level displayed on the horizontal axis. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. \u003c/p\u003e \u003cp\u003e For most levels, the differences between the returns for men and woman are negligible and close to the national average. The returns to a bachelor\u0026rsquo;s degree are slightly higher for men. The largest difference lies in a professional degree, with men earning about 10% more on average than women. Studies on the income difference between male and female physicians discovered that female physicians earn 12\u0026ndash;13% less than their male counterparts, even after controlling for personal characteristics, age, and specialty (Ohsfeldt \u0026amp; Culler, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1986\u003c/span\u003e ). Studies on the market for lawyers have found that in the legal professions, women earn less than men and are underrepresented in high-paying senior ranks (Hersch, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e ). This may explain this paper\u0026rsquo;s results. McDonald and Thorton\u0026rsquo;s (2007) finding that the gender wage gap is primarily due to differences in college major choice cannot be disregarded, however Bobbitt-Zeher ( \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e ) suggests that this is not the primary reason for the gap. Her research shows that controlling for standardized test scores, college major, and college selectivity, the wage gap at the bachelor\u0026rsquo;s level will still be about \u003cspan\u003e$\u003c/span\u003e4,400 per year. Factors such as family, employment, and marital status play a larger role. \u003c/p\u003e \u003cp\u003eAdditionally, the return for women with less than a year of college in 2019 is negative. This suggests that women work more weeks than the average high school graduate while earning less than that of a high school graduated person. Why this is the case is unknown. A possible explanation may be that these women are working in unpaid or low salary internships during their time in college.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results on returns based on Race\u003c/h2\u003e \u003cp\u003eFor the average returns for people of different race, a regression of the model (1) is run on White, Black and Asian Americans. Hispanics are not included in the estimations because they are coded differently in the Census, which makes comparisions difficult. The returns for White people are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the returns for Black people in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and the returns for Asian people in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e graphically represents the returns between each race.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium for White Americans\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.046***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.046***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.044***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.044***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.044***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.001***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.063***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.065***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.072***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.068***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.070***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.025***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.124***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.118***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.119***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.111***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.112***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.118***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.356***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.349***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.359***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.356***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.360***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.356***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.522***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.523***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.528***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.523***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.524***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.505***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.681***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.693***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.708***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.703***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.693***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.691***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.709***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.722***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.730***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.729***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.739***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.700***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,100,861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e991,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,119,613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,142,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,162,665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,180,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium for Black Americans\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.046***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.014***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.033***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.018***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.022***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.064***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.058***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.045***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.054***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.052***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.023***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.114***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.103***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.090***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.095***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.086***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.101***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.304***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.295***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.294***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.301***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.286***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.307***\u003c/p\u003e \u003cp\u003e(.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.463***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.451***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.454***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.435***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.455***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.436***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.635***\u003c/p\u003e \u003cp\u003e(.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.558***\u003c/p\u003e \u003cp\u003e(.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.594***\u003c/p\u003e \u003cp\u003e(.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.596***\u003c/p\u003e \u003cp\u003e(.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.611***\u003c/p\u003e \u003cp\u003e(.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.602***\u003c/p\u003e \u003cp\u003e(.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.611***\u003c/p\u003e \u003cp\u003e(.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.625***\u003c/p\u003e \u003cp\u003e(.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.600***\u003c/p\u003e \u003cp\u003e(.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.607***\u003c/p\u003e \u003cp\u003e(.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.640***\u003c/p\u003e \u003cp\u003e(.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.589***\u003c/p\u003e \u003cp\u003e(.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139,316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121,863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141,888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141,215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e139,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium for Asian Americans\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.031***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.049***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.028***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.008***\u003c/p\u003e \u003cp\u003e(.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.059***\u003c/p\u003e \u003cp\u003e(.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.042***\u003c/p\u003e \u003cp\u003e(.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.058***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.062***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.059***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.060***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.057***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.034***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.116***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.129***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.134***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.108***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.112***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.133***\u003c/p\u003e \u003cp\u003e(.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.323***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.327***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.332***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.319***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.342***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.361***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.518***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.533***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.531***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.521***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.547***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.559***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.662***\u003c/p\u003e \u003cp\u003e(.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.707***\u003c/p\u003e \u003cp\u003e(.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.715***\u003c/p\u003e \u003cp\u003e(.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.688***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.695***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.724***\u003c/p\u003e \u003cp\u003e(.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.667***\u003c/p\u003e \u003cp\u003e(.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.680***\u003c/p\u003e \u003cp\u003e(.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.731***\u003c/p\u003e \u003cp\u003e(.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.697***\u003c/p\u003e \u003cp\u003e(.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.725***\u003c/p\u003e \u003cp\u003e(.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.716***\u003c/p\u003e \u003cp\u003e(.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82,775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89,375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95,454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98,585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e103,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 3 compares the coefficients under the average return column in Tables 4, 5, and 6. The average general returns are shown as a comparison. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. \u003c/p\u003e \u003cp\u003eWhile a neglible return difference exists between White and Asian people, with the returns for White people nearly mirroring the national average and Asians having a slightly lower return on average, the difference between these two groups and Black people is clearly observable. Black Americans earn about 6\u0026ndash;7% less than White Americans for a bachelor\u0026rsquo;s and master\u0026rsquo;s degree, and nearly 10% less than White people for a professional and doctoral degree. This is consistent with previous research that find lower wages for Black individuals using the Bureau of Labor Statistics March 2013 Current Population Survey (Kamara, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Other studies have also found that while the wage gap for Hispanic and Asian men are attributable primarily to educational and English proficiency differences, these factors only explain a quarter of the wage gap for Black men (Black et. al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The Black-White wage gap seems to increase with an increase in proportion of Black people in local populations (Beggs, Villemez, and Arnold, 1997). Huffman and Cohen (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) found that this is because a greater Black population is postively correlated with higher exclusion of Black workers from higher-paying jobs.\u003c/p\u003e \u003cp\u003eThe return for Black Americans with less than a year of college in 2019 is negative, like that of the 2019 premium for women. This once again may be that Black Americans are working in unpaid or low salary internships during their time in college more and receive lower salaries than the average high school graduate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Results on returns based on Citizenship\u003c/h2\u003e \u003cp\u003eFor the average premium for people of different citizenship status in the U.S., model (1) is run on people of US-born citizenship, naturalized citizenship, and non-citizenship statuses. A non-citizen, according to the US Census Bureau, includes legal permanent residents, undocumented residents, temporary and humanitarian migrants, and temporary residents, and foreign students (US Census Bureau, 2006). The returns for US-born citizens are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the returns for naturalized citizens in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, and the returns for non-citizens in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a graphical comparison of the returns for people of different citizenship status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium for US-Born Citizens\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.047***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.042***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.042***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.039***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.041***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.013***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.065***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.065***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.068***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.066***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.066***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.026***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.125***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.115***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.117***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.110***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.110***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.120***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.358***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.348***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.361***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.356***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.362***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.363***\u003c/p\u003e \u003cp\u003e(.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.521***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.521***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.525***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.519***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.521***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.505***\u003c/p\u003e \u003cp\u003e(.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.673***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.680***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.700***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.687***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.680***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.678***\u003c/p\u003e \u003cp\u003e(.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.717***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.728***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.736***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.734***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.744***\u003c/p\u003e \u003cp\u003e(.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.715***\u003c/p\u003e \u003cp\u003e(.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,178,825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,056,981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,199,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,221,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,238,262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,254,985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage Yearly Premium for Naturalized Citizens\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.042***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.049***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.048***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.033***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.036***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.026***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.062***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.068***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.094***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.061***\u003c/p\u003e\n \u003cp\u003e(.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.059***\u003c/p\u003e\n \u003cp\u003e(.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.033***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAssociates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.108***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.109***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.109***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.093***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.097***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.063***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.283***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.292***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.293***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.281***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.269***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.259***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.460***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.472***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.484***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.476***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.480***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.457***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.714***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.716***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.737***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.703***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.698***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.710***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.674***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.675***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.729***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.686***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.706***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.662***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e93,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e89,094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e100,118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e104,683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e107,766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110,785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Yearly Premium for Non-citizens\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.022***\u003c/p\u003e \u003cp\u003e(.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.038***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.050***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.053***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.050***\u003c/p\u003e \u003cp\u003e(.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024***\u003c/p\u003e \u003cp\u003e(.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.018***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.016***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.028***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.031***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.035***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.007***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.075***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.089***\u003c/p\u003e \u003cp\u003e(.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.072***\u003c/p\u003e \u003cp\u003e(.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.070***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.061***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.054***\u003c/p\u003e \u003cp\u003e(.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.242***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.247***\u003c/p\u003e \u003cp\u003e(.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.240***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.244***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.249***\u003c/p\u003e \u003cp\u003e(.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.185***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.439***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.450***\u003c/p\u003e \u003cp\u003e(.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.451***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.447***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.462***\u003c/p\u003e \u003cp\u003e(.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.423***\u003c/p\u003e \u003cp\u003e(.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.575***\u003c/p\u003e \u003cp\u003e(.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.612***\u003c/p\u003e \u003cp\u003e(.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.624***\u003c/p\u003e \u003cp\u003e(.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.653***\u003c/p\u003e \u003cp\u003e(.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.621***\u003c/p\u003e \u003cp\u003e(.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.593***\u003c/p\u003e \u003cp\u003e(.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.444**\u003c/p\u003e \u003cp\u003e(.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.444**\u003c/p\u003e \u003cp\u003e(.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.415**\u003c/p\u003e \u003cp\u003e(.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.437**\u003c/p\u003e \u003cp\u003e(.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.463**\u003c/p\u003e \u003cp\u003e(.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.386**\u003c/p\u003e \u003cp\u003e(.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66,160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71,160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70,778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 4 compares the returns for US-born citizens (in red) with naturalized citizens (in blue) and non-citizens (in yellow). It displays the coefficients under the average column in Tables 7, 8, and 9 on a bar graph. The average general returns are shown as a comparison. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. \u003c/p\u003e \u003cp\u003eThe returns for citizenship status generally show a downwards trend from a US-born citizen to a non-citizen, with US-born citizens earning about the national average for each level of tertiary education, naturalized citizens earning slightly less, and non-citizens earning significantly less, except at the doctoral level. A non-citizen earns 12% less for a bachelor\u0026rsquo;s degree and nearly 30% less for a professional degree than a US citizen. Sumption and Flamm\u0026rsquo;s (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) research arrived at a similar conclusion. This result is likely because non-citizens face work restrictions. U.S. immigration laws purposefully make it difficult for non-citizens to obtain work in the United States to protect the opportunities for U.S. workers (Ingraham, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The federal government limits the ways non-citizens can obtain work authorization in the U.S. and sets time limits on most authorized work. Employers may need to sponsor noncitizens for work visas, which requires paperwork with the USCIS and filing fees, further adding to the costs and barriers for non-citizens to obtain work sponsorship. Furthermore, non-citizens have limited opportunity in the labor market since their visa status is tied to their employer, making it difficult to compete across different vacancies.\u003c/p\u003e \u003cp\u003eAdditionally, the average yearly premium for noncitizens with less than a year of college in 2019 is negative. This suggests once again that these individuals work more weeks than the average high school graduate while earning less than that of a high school graduated person.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Results on returns based on Occupation\u003c/h2\u003e \u003cp\u003eFor the average returns to different occupation types, a regression of model (1) is run on people in management, business, healthcare, legal, computer, and engineering occupations. Under business occupations includes finance occupations, under engineering occupations includes architecture occupations, and under computer occupations also includes math occupations. The returns for these occupations are shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e graphically compares the returns for each occupation against the general returns.\u003c/p\u003e\n\u003ctable id=\"Tab10\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage Yearly Premium for Different Occupations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eManagement Occupations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.115***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.105***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.110***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.116***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.099***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.088***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.156***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.140***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.147***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.150***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.133***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.140**\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssociates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.186***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.158***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.146***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.143***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.128***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.143***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.513***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.500***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.514***\u003c/p\u003e\n \u003cp\u003e(.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.508***\u003c/p\u003e\n \u003cp\u003e(.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.496***\u003c/p\u003e\n \u003cp\u003e(.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.510***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.688***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.679***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.682***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.680***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.676***\u003c/p\u003e\n \u003cp\u003e(.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.695***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.794***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.794***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.797***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.789***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.778***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.810***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.765***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.761***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.759***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.745***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.774***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.750***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142,716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132,048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152,029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157,916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154,228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166,704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eBusiness Occupations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.055***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.040***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.042***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.050***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.059***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.034***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.088***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.089***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.099***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.073***\u003c/p\u003e\n \u003cp\u003e(.0012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.089***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.066***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssociates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.068***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.076***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.072***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.082***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.079***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.088***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.389***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.385***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.401***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.403***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.422***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.429***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.550***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.564***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.573***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e. 568***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.600***\u003c/p\u003e\n \u003cp\u003e(.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.608***\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.605***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.699***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.648***\u003c/p\u003e\n \u003cp\u003e(.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.703***\u003c/p\u003e\n \u003cp\u003e(.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.715***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.717***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.626***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.635 ***\u003c/p\u003e\n \u003cp\u003e(.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.631***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.661***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.669***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.680***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70,727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73,784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76,618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124,597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128,895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eHealth Occupations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.105***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.101***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.116***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.049***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.100***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.082***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.162***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.150***\u003c/p\u003e\n \u003cp\u003e(.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.175***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.014***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.134***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.129***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAssociates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.405***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.386***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.409***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.370***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.374***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.374***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.578***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.565***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.578***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.560***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.576***\u003c/p\u003e\n \u003cp\u003e(.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.577***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.696***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.700***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.735***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.698***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.709***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.700***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.972***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.977***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.041***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.971***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.984***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.978***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e1.098***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.129***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.164***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.105***\u003c/p\u003e\n \u003cp\u003e(.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.146***\u003c/p\u003e\n \u003cp\u003e(.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.124***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e87,890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79,417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91,062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94,854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97,368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99,195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eLegal Occupations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.006***\u003c/p\u003e\n \u003cp\u003e(.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.021***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.043***\u003c/p\u003e\n \u003cp\u003e(.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.045***\u003c/p\u003e\n \u003cp\u003e(.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.048***\u003c/p\u003e\n \u003cp\u003e(.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.025***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.012***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.026***\u003c/p\u003e\n \u003cp\u003e(.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.028***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.025***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.037***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.050***\u003c/p\u003e\n \u003cp\u003e(.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAssociates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.133***\u003c/p\u003e\n \u003cp\u003e(.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.107***\u003c/p\u003e\n \u003cp\u003e(.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.105***\u003c/p\u003e\n \u003cp\u003e(.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.079***\u003c/p\u003e\n \u003cp\u003e(.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.112***\u003c/p\u003e\n \u003cp\u003e(.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.078***\u003c/p\u003e\n \u003cp\u003e(.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.286***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.275***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.275***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.278***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.285***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.256***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.434***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.427***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.442***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.433***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.437***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.398***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.484***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.416***\u003c/p\u003e\n \u003cp\u003e(.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.455***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e435***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.487***\u003c/p\u003e\n \u003cp\u003e(.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.414***\u003c/p\u003e\n \u003cp\u003e(.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.371***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.369***\u003c/p\u003e\n \u003cp\u003e(.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.390***\u003c/p\u003e\n \u003cp\u003e(.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.393***\u003c/p\u003e\n \u003cp\u003e(.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.396***\u003c/p\u003e\n \u003cp\u003e(.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.294***\u003c/p\u003e\n \u003cp\u003e(.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003cp\u003e26,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003cp\u003e24,050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003cp\u003e27,049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003cp\u003e28,069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003cp\u003e31,033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003cp\u003e33,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eComputer Occupations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.111***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.034***\u003c/p\u003e\n \u003cp\u003e(.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.085***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.040***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.033***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.083***\u003c/p\u003e\n \u003cp\u003e(.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.110***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.041***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.090***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.091***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.086***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAssociates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.099***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.040***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.085***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.067***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.083***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.165***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.383***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.313***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.378***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.368***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.401***\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.482***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.502***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.429***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.500***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.487***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.530***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.620***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.636***\u003c/p\u003e\n \u003cp\u003e(.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.555***\u003c/p\u003e\n \u003cp\u003e(.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.643***\u003c/p\u003e\n \u003cp\u003e(.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.658***\u003c/p\u003e\n \u003cp\u003e(.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.711***\u003c/p\u003e\n \u003cp\u003e(.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.829***\u003c/p\u003e\n \u003cp\u003e(.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.447***\u003c/p\u003e\n \u003cp\u003e(.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.340***\u003c/p\u003e\n \u003cp\u003e(.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.466***\u003c/p\u003e\n \u003cp\u003e(.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.492***\u003c/p\u003e\n \u003cp\u003e(.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.529***\u003c/p\u003e\n \u003cp\u003e(.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.563***\u003c/p\u003e\n \u003cp\u003e(.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e40,407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44,688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47,111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49,985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eEngineering Occupations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026nbsp;year.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.072***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.054***\u003c/p\u003e\n \u003cp\u003e(.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.063***\u003c/p\u003e\n \u003cp\u003e(.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.047***\u003c/p\u003e\n \u003cp\u003e(.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.094***\u003c/p\u003e\n \u003cp\u003e(.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.082***\u003c/p\u003e\n \u003cp\u003e(.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eCollege\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year. no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.059***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.131***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.090***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.083***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.133***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.060***\u003c/p\u003e\n \u003cp\u003e(.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAssociates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.117***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.150***\u003c/p\u003e\n \u003cp\u003e(.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.125***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.113***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.154***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.173***\u003c/p\u003e\n \u003cp\u003e(.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.461***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.483***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.473***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.460***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.513***\u003c/p\u003e\n \u003cp\u003e(.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.559***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.613***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.624***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e. 598***\u003c/p\u003e\n \u003cp\u003e(.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.596***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.649***\u003c/p\u003e\n \u003cp\u003e(.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.715***\u003c/p\u003e\n \u003cp\u003e(.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.736***\u003c/p\u003e\n \u003cp\u003e(.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.756***\u003c/p\u003e\n \u003cp\u003e(.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.797***\u003c/p\u003e\n \u003cp\u003e(.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.756***\u003c/p\u003e\n \u003cp\u003e(.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.827***\u003c/p\u003e\n \u003cp\u003e(.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.849***\u003c/p\u003e\n \u003cp\u003e(.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eProfessional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e.424***\u003c/p\u003e\n \u003cp\u003e(.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.551***\u003c/p\u003e\n \u003cp\u003e(.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.540***\u003c/p\u003e\n \u003cp\u003e(.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e. 435***\u003c/p\u003e\n \u003cp\u003e(.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.539***\u003c/p\u003e\n \u003cp\u003e(.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.599***\u003c/p\u003e\n \u003cp\u003e(.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"\"\u003e\n \u003cp\u003e27,247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30,561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: Standard errors in parentheses. N indicates the number of data points. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. In the above regressions, sex, age, age squared, race, citizenship, marital status, same sex household, English ability, disability, occupation industry, class of worker, working hours, weeks worked, and family income were all included as control variables except in 2019, where same sex and WKW were dropped.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 5 compares the returns for management, business, health, law, computer, and engineering occupational returns against the average general returns. It displays the coefficients under the average column in Table 10 for each occupation and level of education. The returns at each level are displayed as a percentage earned above someone with a high school diploma/GED certification. The values used in this figure are the average returns across all six years. \u003c/p\u003e \u003cp\u003eGenerally, at each level of higher education, Healthcare professions have the greatest wage increase across all levels followed by Management professions. Law professions seem to see the lowest wage increase compared to the other occupations at every level.\u003c/p\u003e \u003cp\u003eFor a college education with no degree, the occupations with the highest return (average of 14%) are Healthcare and Management. Law occupations receive the lowest wage increase, at a 2% increase from someone with a high school diploma. For a bachelor\u0026rsquo;s degree, master\u0026rsquo;s, professional and doctoral degree, the trend of Healthcare and Management professions seeing the highest returns whereas Law sees the lowest holds. Returns for Healthcare peak at the professional level, at nearly 115% wage increase relative to someone with just a high school diploma.\u003c/p\u003e \u003cp\u003e The results we find are consistent with previous literature suggesting that returns will vary depending on the occupation (Oreopoulos and Petronijevic, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e ). Ketel et. al. (2013) studied the returns to a medical school in the Netherlands and determined that every year post graduation, doctors earn at least 20% more than people who end up in their next-best occupation. Simkovic and McIntyre ( \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e ) used both a present value and annual earning calculation to estimate the economic value of a law degree. They found that a law degree has mean annual earnings premium of approximately \u003cspan\u003e$\u003c/span\u003e57,200 in 2013 dollars, and the mean lifetime value of a law degree is approximately \u003cspan\u003e$\u003c/span\u003e1\u0026nbsp;million. These numbers, however, don\u0026rsquo;t seem align with our findings. \u003c/p\u003e \u003cp\u003eFor our regression, the legal returns are significantly lower than that of the other jobs and the national average. This may be because people in legal occupations earn less than what is expected. Another hypothesis is that there are so many law graduates that the supply drives the wage premium down. The lower return at the professional compared to the master\u0026rsquo;s and doctoral education level is also confusing given that the juris doctor degree is a professional degree and the degree someone needs in order to practice law. These differences, however, are likely due to the nature of the dataset and how the Census categorizes reported occupations. Partners at law firms are the highest paid lawyers and may be categorized under management occupations because their duties more closely parallel managing tasks (managing the firm, establishing financial and operational strategies, etc.) despite being lawyers\u003csup\u003e7\u003c/sup\u003e. This, however, may not explain all the counterintuitive trends in the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Conclusion\u003c/h2\u003e \u003cp\u003eTo conclude, regardless of sex, race, and citizenship status, one can expect to earn higher annual financial returns on average with higher levels of education. Significant wage increases occur once someone obtains a bachelor\u0026rsquo;s degree or higher. However, there are variations in the benefits to people of different race and citizenship status. The gender wage gap, as suggested by existing literature, is relatively small but exists at the professional level. There is a significant premium differential between Black and White/Asian Americans, and between citizens and non-citizens, even when controlling for occupation and education differences. Different occupations also have varying returns to different levels of tertiary education. This suggests that occupational choice plays a huge role in wage determination.\u003c/p\u003e \u003cp\u003eAlthough existing literature gives us some direction as to why these trends exist, more research needs to be done to conclusively determine the underlying causes of the observed wage gaps. Specifically, further research should be pursued on determining the cause of the gender wage gap, the racial gap, and the wage premium to US citizenship. Furthermore, gaps between existing findings and our own, such as the returns to a college degree, need explanations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis manuscript is written under the guidance of AO by EL.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNo one else contributed towards writing of this manuscript.\u003c/p\u003e\u003cp\u003eNo funding was received in preparing this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAltonji, J. G., \u0026amp; Zimmerman, S. D. (2017). \u003cem\u003eThe costs of and net returns to college major\u003c/em\u003e (No. w23029). National Bureau of Economic Research.\u003c/li\u003e\n\u003cli\u003eBarro, R. J., \u0026amp; Lee, J. W. (2013). A new data set of educational attainment in the world, 1950\u0026ndash;2010. \u003cem\u003eJournal of development economics,\u003c/em\u003e 104, 184-198.\u003c/li\u003e\n\u003cli\u003eBeggs, John J., Wayne J. Villemez, and Ruth Arnold. (1997). Black Population Concentration and Black-White Inequality: Expanding the Consideration of Place and Space Effects. \u003cem\u003eSocial Forces,\u003c/em\u003e 76, 65\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eBlack, D., Haviland, A., Sanders, S., \u0026amp; Taylor, L. (2006). Why do minority men earn less? A study of wage differentials among the highly educated. \u003cem\u003eThe Review of Economics and Statistics, 88\u003c/em\u003e(2), 300-313.\u003c/li\u003e\n\u003cli\u003eBoarini, R., \u0026amp; Strauss, H. (2010). What is the private return to tertiary education? New evidence from 21 OECD countries. \u003cem\u003eOECD Journal: Economic Studies, 2010.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBobbitt-Zeher, D. (2007). The Gender Income Gap and the Role of Education. \u003cem\u003eSociology of \u003c/em\u003e\u003cem\u003eEducation, \u003c/em\u003e80, 1\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eCarnevale, A. P., Cheah, B., \u0026amp; Rose, S. J. (2011). The College Payoff: Education, Occupations, Lifetime Earnings, report prepared for the Center on Education and the Workforce.\u003c/li\u003e\n\u003cli\u003eDale, S. B., \u0026amp; Krueger, A. B. (2002). Estimating the payoff to attending a more selective college: An application of selection on observables and unobservables. \u003cem\u003eThe Quarterly Journal of Economics, \u003c/em\u003e117(4), 1491-1527.\u003c/li\u003e\n\u003cli\u003eGoldin, C. (2004.) The Long Road to the Fast Track: Career and Family. \u003cem\u003eAnnals of the American \u003c/em\u003e\u003cem\u003eAcademy of Political and Social Science, \u003c/em\u003e596(1), 20\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eGoldin, C. (2006.) The Quiet Revolution That Transformed Women\u0026rsquo;s Employment, Education, and Family. \u003cem\u003eAmerican Economic Review,\u003c/em\u003e 96(2), 1\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eHersch, J. (2003). The New Labor Markets for Lawyers: Will Female Lawyers Still Earn Less. \u003cem\u003eCardozo Women\u0026apos;s LJ, 10,\u003c/em\u003e 1.\u003c/li\u003e\n\u003cli\u003eHout, M. (2012). Social and economic returns to college education in the United States\u003cem\u003e. Annual \u003c/em\u003e\u003cem\u003ereview of sociology,\u003c/em\u003e 38, 379-400.\u003c/li\u003e\n\u003cli\u003eHuffman, M., \u0026amp; Cohen P. (2004). Racial Wage Inequality: Job segregation and Devaluation across U.S. Labor Markets. \u003cem\u003eAJS\u003c/em\u003e, 109(4), 902\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eIngraham, M. (2011). \u003cem\u003eCitizenship Guide: Hiring Non-Citizens. \u003c/em\u003eBernard Koteen Office of Public Interest Advising, Harvard Law School \u003c/li\u003e\n\u003cli\u003eKamara, J. (2015). Decomposing the Wage Gap: Analysis of the Wage Gap Between Racial and Ethnic Minorities and Whites. \u003cem\u003ePepperdine Policy Review\u003c/em\u003e, 8(1).\u003c/li\u003e\n\u003cli\u003eKetel, N., Leuven, E., Oosterbeek, H., \u0026amp; van der Klaauw, B. (2016). The returns to medical school: Evidence from admission lotteries. \u003cem\u003eAmerican Economic Journal: Applied Economics\u003c/em\u003e, 8(2), 225-54.\u003c/li\u003e\n\u003cli\u003eSimkovic, M., \u0026amp; McIntyre, F. (2014). The economic value of a law degree. \u003cem\u003eThe Journal of Legal \u003c/em\u003e\u003cem\u003eStudies,\u003c/em\u003e 43(2), 249-289.\u003c/li\u003e\n\u003cli\u003eMaurin E., McNally S. (2008.) 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Retrieved from nces.ed.gov/surveys/npsas.\u003c/li\u003e\n\u003cli\u003eWeiss, A. (1995.) Human Capital vs. Signalling Explanations of Wages. \u003cem\u003eJournal of Economic \u003c/em\u003e\u003cem\u003ePerspectives, \u003c/em\u003e9(4), 133\u0026ndash;154.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e[3] The occupations examined in this paper are management occupations, business and finance occupations, health occupations, legal occupations, computer and math occupations, and engineering and architecture occupations as classified by the US Census Bureau.\u0026nbsp;\u003c/p\u003e\u003cp\u003e[4] Business cycle state and other cohort variations are not factored out of these numbers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[5] \u0026nbsp;We ran the 2018 regression without weeks worked as a control and obtained nearly identical numbers to the current 2019 returns\u003c/p\u003e\n\u003cp\u003e[6] Other studies typically use a present-value calculation, and the calculated returns are for a life cycle of earnings\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[7] According to the subject matter experts at the US Census Bureau, occupations are coded based on what the respondent describes as their most important duties.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-economics-race-and-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jerp","sideBox":"Learn more about [Journal of Economics, Race, and Policy](https://link.springer.com/journal/41996)","snPcode":"41996","submissionUrl":"https://submission.nature.com/new-submission/41996/3","title":"Journal of Economics, Race, and Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6866499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6866499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudies that quantify the wage premium to different levels of tertiary education and compare them across people of different sex, races, citizenship statuses, and occupations are rare. In this paper, we compare the average yearly income margin of different levels of tertiary education, irrespective of major or attended school, to that of a person who completed only secondary education. We focus on the following levels of tertiary education: college without a degree, an associate, bachelor\u0026rsquo;s, master\u0026rsquo;s, doctoral, and professional degrees. Using micro data from the US Census Bureau from the years 2014 to 2019 and the Mincer equation, we estimate a regression for each gender, race, citizenship, and occupation category. The earnings premium for the different levels of tertiary education, each compared to the earnings of someone with no tertiary education, are as follows: less than a year of college without a degree: 3.8; some years of college without a degree: 5.8%; associate: 11.3%; bachelor\u0026rsquo;s: 34.6%; master\u0026rsquo;s: 51.5%; doctoral: 68.6%; and professional: 71.3%.\u003c/p\u003e","manuscriptTitle":"Returns to Tertiary Education and the Differences by Gender, Race, Citizenship, and Occupation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 11:26:55","doi":"10.21203/rs.3.rs-6866499/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-14T15:43:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T15:30:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T13:28:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T22:15:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262498672608188686128001978327396527770","date":"2025-07-01T12:56:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121580782215842951200069742354167675639","date":"2025-06-27T14:01:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175755586694449221031562833947037698519","date":"2025-06-26T14:48:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T13:51:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-25T13:42:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-25T03:49:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Economics, Race, and Policy","date":"2025-06-10T22:52:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-economics-race-and-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jerp","sideBox":"Learn more about [Journal of Economics, Race, and Policy](https://link.springer.com/journal/41996)","snPcode":"41996","submissionUrl":"https://submission.nature.com/new-submission/41996/3","title":"Journal of Economics, Race, and Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"51a02587-e7f7-42ba-aa79-6aa41c30cc99","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-08-14T15:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 11:26:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6866499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6866499","identity":"rs-6866499","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}