Field of Study in College and Lifetime Earnings in the United States

Abstract

Our understanding about the relationship between education and lifetime earnings often neglects differences by field of study. Utilizing data that match respondents in the Survey of Income and Program Participation to their longitudinal earnings records based on administrative tax information, we investigate the trajectories of annual earnings following the same individuals over 20 years and then estimate the long-term effects of field of study on earnings for U.S. men and women. Our results provide new evidence revealing large lifetime earnings gaps across fields of study. We show important differences in individuals’ earnings trajectories across different stages of the work life by field of study. In addition, the gaps in 40-year (i.e., ages 20 to 59) median lifetime earnings among college graduates by field of study are larger, in many instances, than the median gap between high school graduates and college graduates overall. We also find significant variation among graduate degree holders. Our results uncover important similarities and differences between men and women with regard to the long-term earnings differentials associated with field of study. In general, these findings underscore field of study as a critical dimension of horizontal stratification in educational attainment.

Keywords

lifetime earnings educational attainment field of study semisynthetic cohort method

Education plays an increasingly important role in shaping social stratification and inequality in the United States. A large literature shows that college graduates earn more, have higher-status jobs, and are more likely to be employed than those without a college degree (Autor 2014; Brand and Xie 2010; Fischer and Hout 2006; Kim and Sakamoto 2008; Oreopoulos and Petronijevic 2013). However, not all college degrees have similar economic returns. Although most studies assume homogeneity in the financial return to education, recent research increasingly calls attention to the role of horizontal stratification in higher education (for a review, see Gerber and Cheung 2008). A small but growing body of evidence shows important labor market differentials across fields of study (e.g., Berger 1988; Reed and Miller 1970; Rumberger and Thomas 1993; Song and Glick 2004; Thomas 2000). As the proportion of the population who completes a college degree increases, an important research question is whether horizontal stratification in education is becoming more substantial than vertical stratification in determining financial rewards in the labor market over the life course and, ultimately, life chances.

Lifetime earnings are a critical dimension in the process of social stratification of life chances and well-being (Weber [1922] 1978). Lifetime earnings measure the accumulation of rewards in the labor market over a career. They are a consequential determinant of wealth and savings (Engen, Gale, and Uccello 2005; Ruel and Hauser 2013), retirement income security and Social Security benefit levels (Iams, Reznik, and Tamborini 2010), health and mortality (Cristia 2009; Waldron 2013), and various aspects of social mobility (Hendricks 2007). Research has firmly established a positive relationship between education and earnings. Less understood, although generally recognized, is the extent to which the long-run economic returns to college vary across fields of study. This is partly due to the scarcity of long-term longitudinal data (Cooke 2003; Elder and Pavalko 1993) and also the lack of information on field of study in most national surveys. Little is known about the role played by field of study in determining lifetime earnings, how earnings differentials across field of study might evolve differently over work careers, and how these outcomes vary by gender.

To help address these shortcomings, we use a rich data set that matches a nationally representative sample of respondents from the Survey of Income and Program Participation (SIPP) with their longitudinal earnings based on administrative tax information compiled at the Social Security Administration (SSA). Given the lack of research on lifetime earnings, the main objective of this study is to provide baseline estimates of the association between field of study and lifetime earnings. This is the first study to use nationally representative survey data matched to longitudinal earnings data spanning a long stretch of the same individual’s life to document how lifetime earnings vary by field of study. The analysis also extends our knowledge about differences in the lifetime financial returns to graduate education by field of study. We demonstrate that field of study, not just educational level, is associated with age-differentiated earnings trajectories over the work life. In addition, we highlight important gender differences in the lifetime financial returns of college education by field of study. No prior study has investigated how gender differences in horizontal differentiation change over the work career. This study advances our understanding of the central role of horizontal stratification in higher education in determining labor market outcomes over the life course.

Literature Review

Rising Importance of Horizontal Stratification in Education

The most common approach to understanding the labor market effects of educational attainment focuses on the highest level completed as an ordinal outcome (Brand and Xie 2010; Buchmann and DiPrete 2006; Mare 1980). However, given rising rates of college completion and increases in wage inequality within almost all demographic groups over recent decades (Autor 2014), other dimensions of educational attainment may be increasingly important sources of earnings differentials. The socioeconomic differentiation associated with field of study and college type is now widely recognized as an important feature of the horizontal dimension of educational stratification (Davies and Guppy 1997; Daymont and Andrisani 1984; Gerber and Cheung 2008; Ma and Savas 2014; Rumberger and Thomas 1993; Song and Glick 2004; Thomas 2000).

Differences in earnings by field of study over the life course can be thought of as one way horizontal stratification in education has long-run impacts on labor market outcomes. Why should lifetime earnings vary by one’s field of study? Human capital perspectives tend to view differences in earnings across college majors as resulting from different types of skills acquired in college programs. These skill sets, in turn, lead to differences in levels of productivity and marketable human capital (Daymont and Andrisani 1984; Shauman 2006). The skill-biased technological change perspective emphasizes the role of technological changes in raising the productivity of certain work-related skills (e.g., computer programming) that employers increasingly reward over other skills (e.g., cultural understanding). This perspective suggests that as technology advances and skill demand increase, the relative importance of horizontal stratification by field of study will rise as well.

A social-closure view (Weeden 2002) attributes earnings differentials by field of study to the control of supply using positional power (i.e., control over the number admitted). With the financially lucrative fields, such as engineering, raising the admission bar in many institutions while tertiary education expands, thus lowering the bar to higher education, the social-closure perspective predicts the rising importance of horizontal inequality by major field in educational stratification.

In addition, fields of study may effectively sort students by ability. The expansion in higher education has increased differences in mean ability between fields of study, leading to increases in labor market inequality between university graduates from different fields (Reimer, Noelke, and Kucel 2008). Selection factors into college majors reflect another pathway by which field of study may influence earnings. Students who have strong preferences toward higher earnings may select into certain majors with the anticipation of future higher earnings (Beffy, Fougere, and Maurel 2012; Eide 1994). Individual decisions about how much to invest in higher education and what fields to choose are partially contingent on expectations of future earnings streams (Arcidiacono, Hotz, and Kang 2012; Oreopoulos and Petronijevic 2013).

Together, these theoretical views predict substantial gaps in earnings by field of study. Yet, the relative importance of horizontal versus vertical dimensions of educational stratification on long-term labor market outcomes, such as lifetime earnings, has not been adequately addressed.

Prior Estimates on Lifetime Returns to Education by Field of Study

These theoretical perspectives point toward field of study as having long-term impacts on financial rewards in the labor market for college graduates. Yet very few sociological studies investigate even the effects of highest educational level on long-term earnings (see, however, Tamborini, Kim, and Sakamoto 2015). Several recent technical reports provide estimates of lifetime earnings by highest level of educational attainment (Carnevale, Rose, and Cheach 2011; Day and Newburger 2002; Mitchell 2014). However, previous analyses have not examined differences in lifetime earnings by field of study, apart from a handful of notable exceptions that use the American Community Survey (ACS) to estimate lifetime earnings by college major (Carnevale, Cheah, and Hansen 2015; Herschbein and Kearney 2014; Julian 2012). Although useful, the ACS provides information only on respondents’ undergraduate degree and is cross-sectional in design.

The paucity of research on the relationship between field of study and lifetime earnings is due primarily to the lack of data on long-term earnings and field of study in most surveys. In the extant literature, most analyses rely on cross-sectional data and use a synthetic cohort method to generate “lifetime” estimates (e.g., Baum and Ma 2007; Carnevale et al. 2011; Day and Newburger 2002; Julian and Kominski 2011; Kantrowitz 2007). A simple synthetic cohort that cumulates the annual earnings of workers of different ages often assumes that workers are employed full-time and full-year for their entire work careers and that the earnings of older cohorts apply to younger cohorts. This practice neglects the employment issue and thus may lead to measurement errors in dependent variables (Haider and Solon 2006). Lifetime earnings are a function of annual (or other short-term) earnings and employment stability. Estimates using cross-sectional data can account for the former but neglect the latter. Moreover, the association between short-term and lifetime earnings may vary significantly, particularly at younger ages (Björklund 1993).

Career Volatility and Earnings Differentials by Field of Study

Additional theoretical and empirical issues revolve around the relationship between lifetime earnings and field of study. Earnings and job mobility vary not only by education but also over the life course (Moffitt and Gottschalk 2011; Riddell and Song 2011). Consequently, extrapolations of cross-sectional data for lifetime outcomes may mistakenly construe career volatility as representing inequality in long-term earnings. In populations with high intragenerational income mobility, individuals’ earnings over a short time frame may differ from a longer time frame. Previous studies finding lower earnings dispersion when measures account for longer periods of time reinforce this point (Bowlus and Robin 2004; Huggett, Ventura, and Yaron 2011). Unemployment rates and timing of withdrawal from the labor force differ by level of education (National Center for Education Statistics 2013) and field of study (Carnevale et al. 2015). Insofar as career volatility varies across fields of study, estimated long-term returns based on cross-sectional data do not accurately reflect the lifetime value of different majors.

Studying lifetime earnings across fields of study is also informed by life course perspectives. A life course perspective emphasizes the trajectories of an age-differentiated life course (Sampson and Laub 1992; Warren, Hauser, and Sheridan 2002) and the role of transitions, such as leaving school, marriage, and childbearing, in shaping an individual’s life (Elder, Johnson, and Crosnoe 2003). In this study, we examine how the same individuals’ earnings trajectories evolve differently by field of study as they age. We expect that the earnings differential associated with horizontal stratification in education varies by age. This is partly because horizontal differentials are associated with the timing of life events and transitions.

Age at college graduation affects labor force status and thus lifetime earnings. Workers who enter the labor market right after high school may have more stable and longer durations of employment at early stages of their work career than individuals who enter college, who tend to have limited attachment to labor markets in their early 20s. Furthermore, the length of schooling varies by field of study (Beffy et al. 2012). The duration of study, in turn, may affect both the age and period of labor force entrance. Moreover, a lengthy period of schooling suggests that the earnings gains associated with educational attainment may arise later in the life course. Fields of study that require longer schooling (e.g., medicine) will have lower or even negative effects at the early stage of one’s work career but may have positive effects on earnings at later stages (e.g., earnings growth or retirement timing may differ by field of study). A likely consequence is that earnings differentials by field of study might be larger among some fields at a later stage of the work career than at the early stage.

Childbearing is an important life event in accounting for gender differences. Male high school graduates often start working after graduation, whereas female high school graduates are more likely to be out of the labor force in their 20s due to family-related issues (U.S. Bureau of Labor Statistics 2014). As a result, the relative financial return to higher education for women (relative to high school graduates) is likely to manifest early in their work careers, whereas the return is delayed for men. Thus, we expect that the gender gap in the relative return to higher education, and differences by field of study, will be largest in the 20s and will attenuate with age.

Childbearing may also be associated with gender differences in returns to field of study. The distinction between vertical and horizontal stratification was originally used to explain sex segregation in higher education (Charles and Bradley 2002). A large number of sociological studies have analyzed the impact of field of study on the gender wage gap (e.g., Bobbit-Zeher 2007; Charles and Bradley 2002, 2009; England and Li 2006; Jacobs 1995; S. Morgan et al. 2013; Ramirez and Wotipka 2001; Roksa 2005; Turner and Bowen 1999). Although these studies identify gender segregation in fields of study as a crucial mechanism behind the gender earnings gap, there is evidence that lucrative fields are equally beneficial for men and women (e.g., Ma and Savas 2014). Nonetheless, we expect to find some gender differences by field of study. Women who value traditional gender role specialization (Becker 1981) may concentrate in nonprofessional fields, such as humanities and the liberal arts. The relative return to these fields may be lower for women than for men. Furthermore, we expect horizontal differentials will be greatest at the time of childbearing for the highly educated. College-educated women typically have children when their work careers are on track after completing education (Sawhill 2014). To the extent that the choice of field of study is associated with gender role specialization, we expect horizontal differentials among women will be greatest at the midcareer stage.

Analytic Strategy

Data

The analysis uses the 2004 and 2008 SIPP matched to the Detailed Earnings Record (DER) file at the SSA. SIPP data provide demographic and socioeconomic characteristics of a nationally representative sample. We selected Wave 2 because it includes a one-time topical module that provides retrospective information about respondents’ education. Specifically, we use the SIPP’s Educational History Module to measure field of study and to construct partial proxies for respondents’ background educational characteristics. We pooled Wave 2 of the 2004 and 2008 SIPP panels to acquire sufficient sample sizes of fields of study.

The DER file provides respondents’ annual taxable earnings from 1982 to 2008 based on their W-2 tax records. These data begin in 1982 because that is when SSA started to collect reliable full earnings information, beyond the maximum taxable earnings and Social Security–covered employment. Earnings data end in 2008 to minimize effects associated with the Great Recession. We henceforth refer to this matched longitudinal data set as the SIPP-IRS. More detailed descriptions of the SSA administrative records and survey matches may be found elsewhere (see Kim and Tamborini 2014; McNabb et al. 2009; Tamborini and Iams 2011).

The central advantage of the SIPP-IRS data file is the ability to construct an individual’s long-term earnings profile over an age-specific period. These data also have several advantages over other longitudinal data sets. The data set is not limited to particular birth cohorts, and the sample size is fairly large. Moreover, because our base sample comes from Wave 2, sample attrition is minimal. Furthermore, the SIPP-IRS data contain well-measured annual earnings that are not top coded. A possible drawback is that not all SIPP respondents were successfully matched with the administrative data. The share of respondents successfully matched, however, is high at around 80 (2004 SIPP) to 90 (2008 SIPP) percent. Nevertheless, we use a SIPP weight that adjusts for nonmatched respondents to maintain the national representation of the sample.

Analytic Sample

Our main sample consists of college graduates and persons who completed high school from four birth cohorts: 1962 to 1969, 1952 to 1959, 1942 to 1949, and 1932 to 1939. We selected these cohorts to construct age-specific, 10-year cumulative earnings streams at different career stages (for respondents in their 20s, 30s, 40s, and 50s).¹ We track annual earnings of the same individual in each cohort over 20 years and compute two 10-year cumulative earnings blocks therein. We do not assess the entire lifetime earnings of one individual, because the longitudinal earnings data do not extend further back than 1982.

The 10-year cumulative earnings of individuals in each cohort are age specific. For example, 10-year cumulative earnings for the 30s reflect the total annual earnings of an individual from ages 30 to 39. For the 1952 to 1959 birth cohort, the 10-year period for the subset born in 1952 is the sum of annual earnings from 1982 (age 30) to 1991 (age 39). For the subset born in 1953, it is the sum of annual earnings from 1983 (age 30) to 1992 (age 39). We repeat these steps until reaching the final birth year for each cohort. As Table 1 shows, except for the 20s (due to data availability), each 10-year age group consists of two of our birth cohorts to maximize sample size.

Table 1.

Description of Sample by Cohorts.

Variable	Total	Cohort 1	Cohort 2	Cohort 3	Cohort 4
Year of birth	1932-1969	1962-1969	1952-1959	1942-1949	1932-1939
Age in the 2004 SIPP	35-72	35-42	45-52	55-62	65-72
Age in the 2008 SIPP	39-76	39-46	49-56	59-66	69-76
Total covered ages	20-59	20-39	30-49	40-59	50-59
Covered age in the first 10-year cumulative earnings	20-59	20-29	30-39	40-49	50-59
Starting year of the 1^st 10-year cumulative earnings	1982-1989	1982-1989	1982-1989	1982-1989	1982-1989
Ending year of the first 10-year cumulative earnings	1991-1998	1991-1998	1991-1998	1991-1998	1991-1998
Covered age in the second 10-year cumulative earnings	30-59	30-39	40-49	50-59	—
Starting year of the second 10-year cumulative earnings	1992-1999	1992-1999	1992-1999	1992-1999	—
Ending year of the second 10-year cumulative earnings	2001-2008	2001-2008	2001-2008	2001-2008	—
Sample size by age group: Men
Age 20s	4,023	4,023	—	—	—
Age 30s	8,272	4,035	4,237	—	—
Age 40s	7,360	—	4,215	3,145	—
Age 50s	4,665	—	—	3,121	1,544
Total (20s to 50s)	24,320
Sample size by age group: Women
Age 20s	4,237	4,237	—	—	—
Age 30s	8,537	4,147	4,390	—	—
Age 40s	7,550	—	4,408	3,142	—
Age 50s	4,715	—	—	3,028	1,687
Total (20s to 50s)	25,039

As noted, our analytic sample contains college graduates and high school graduates. We excluded high school dropouts and those who have some college education but no bachelor’s degree. We also dropped individuals who obtained a high school degree through the General Educational Development (GED) tests.² Respondents who received a Social Security disability benefit during their 20-year observation period (using an administrative variable merged to our data set) were also excluded.³ Thus, our lifetime earnings estimates are net of disability before entrance into labor markets and the varying likelihood of disability over a life course. We limited the sample to persons who had at least 2 years of positive earnings in each 10-year period, allowing us to remove individuals with very weak labor force attachment.⁴ We restricted the sample to the native born to avoid the complication of assimilation processes and the number of eligible working years in the United States. These selection criteria leave us with a total sample size of 24,320 men and 25,039 women. Among them, 13,014 men and 13,788 women held at least a bachelor’s degree.

Estimation Strategy

We assess 10-year cumulative earnings by field of study over different stages of the life course. The main multivariate model is quantile regression at the median of logged cumulative earnings. Unlike the classical linear model (i.e., ordinary least squares [OLS]), median quantile regression does not assume homoscedasticity and normality. This is advantageous because earnings distribution shapes differ by education and across different stages of the work life. Quantile regression estimates are also characterized by linear equivariance (Hao and Naiman 2007). Because the conditional mean of log earnings is not equivalent to the log of conditional mean earnings, OLS-based estimates of lifetime earnings will suffer from retransformation bias (Manning 1998). In contrast, conditional quantiles possess a monotone equivariance property so that estimates of logged earnings can be retransformed to actual dollars. Moreover, given that the distribution of long-term earnings is extremely skewed to the right for college-educated workers, median lifetime earnings represent typical workers better than mean earnings. Furthermore, because commonly cited estimates of lifetime earnings use median earnings, using median regression models facilitates straightforward comparison of our results with previous estimates.

Our model can be written as follows:

y_{g i} = Σ β_{g f} F S_{g f i} + Σ γ_{g i} X_{g i j} + ε_{g i,}

in which y_gi refers to log-transformed 10-year cumulative earnings for age group g and individual i. Administrative earnings refers to respondents’ annual earnings for all jobs subject to federal income tax, including uncapped wages, salaries, and other compensation, such as bonuses, commissions, tips, and self-employment. All earnings are adjusted to 2010 dollars using the Consumer Price Index (i.e., series CPI-W).

The main independent variable, FS, refers to a set of 15 binary indicators measuring an individual’s field of study at the highest level of educational attainment. For individuals with a bachelor’s degree only, seven fields of study are identified: (1) business; (2) science, technology, engineering, and mathematics (STEM); (3) health science; (4) social science, history, psychology, and communication; (5) education; (6) liberal arts, humanities, art, and architecture; and (7) others. For advanced degree holders, eight fields of study are identified: (1) business; (2) STEM; (3) medicine and dentistry; (4) law; (5) social science, history, psychology, and communication; (6) education; (7) liberal arts, humanities, art, and architecture; and (8) others. FS thus indicates respondents’ level of education and their field of study at their highest degree.⁵ We could not disaggregate graduate degrees by master’s, professional, and doctoral degrees due to inadequate sample sizes. The reference group for FS (i.e., coded 0) is high school graduates. X is a vector of J control variables (discussed below).

Our measures reflect respondents’ highest level of education when the survey was conducted. Therefore, for example, the earnings of advanced degree holders in STEM in their 20s are not necessarily the earnings of persons holding such a degree in their 20s but rather the earnings of persons who eventually obtained an advanced degree in a STEM area at some point of their life. Because age at highest degree may differ by level of education and field of study, the regression models control for age at final degree.

The regression analyses include other control variables, such as sociodemographic controls referring to birth year, race and ethnicity (white, African American, Hispanic, and other), and being born in the South. The models include age at final degree and the survey year, because we pool the data. We also utilize several retrospective measures in the education history module as partial proxies for family background attributes. First, we control for private high school attendance because it is closely associated with family income (National Center for Education Statistics 1997). Second, we measure whether respondents completed college preparation courses and, separately, advanced mathematics or science courses in high school. People with higher socioeconomic family background are more likely to take advanced placement (AP) and college preparatory classes (Espenshade and Radford 2009). Our results thus show the detailed association between lifetime earnings and field of study. We aim not to estimate the causal effects of education but rather to take into account the effects of demographic covariates and equalize the different effects of these covariates across four cohorts in computing lifetime earnings.

Using our estimates of 10-year cumulative earnings for each age group, we then adopt what we refer to as a semisynthetic cohort method to estimate 40-year lifetime earnings. Specifically, we use estimates of the net 10-year cumulative earnings based on the quantile regression at the median (Equation 1) as follows:

L F_{f s} = \exp ({\hat{y}}_{20 s, f s}) + \exp ({\hat{y}}_{30 s, f s}) + \exp ({\hat{y}}_{40 s, f s}) + \exp ({\hat{y}}_{50 s, f s}),

where LF_fs is a 40-year lifetime earnings estimate of field of study at the highest degree fs and $\hat{y}$ _20s,fs, $\hat{y}$ _30s,fs, $\hat{y}$ _40s,fs, and $\hat{y}$ _50s,fs are estimates of 10-year cumulative log earnings at age group by field of study at the highest degree after controlling for X. To estimate $\hat{y}$ , we use the gender-specific grand mean value of X_g across all age groups for all control variables.

This approach does not entirely overcome the problems of synthetic cohort methods. Nonetheless, it provides more realistic estimates of lifetime earnings by field of study than do purely synthetic cohort calculations based on annual earnings observed in cross-section.

Empirical Findings

We begin by tracking annual earnings over a 40-year work life by field of study. Men’s earning trajectories demonstrate the well-known inverted-U-curve pattern regardless of level of education and field of study, but the depth of the curve varies substantially by field of study. Women’s earnings trajectories show a similar inverted-U-curve pattern, but the depth of the curve is much shallower than for men.

Using individual annual administrative earnings records, we compute 10-year cumulative earnings from age 20 to 59 by each individual’s field of study.⁶ We then estimate a set of median quantile regressions for each 10-year earnings block (logged) by the specified age range, after controlling for demographic and high school–related covariates. Tables 2 and 3 show results for men and women, respectively.

Table 2.

Median Regressions of 10-year Cumulative Log-transformed Earnings by Age Group, Men.

	Age 20s		Age 30s		Age 40s		Age 50s
Degree (Reference = High School Graduate)	Coefficient	(SE)	Coefficient	(SE)	Coefficient	(SE)	Coefficient	(SE)
BA
Business	.154	(.039)***^a,b	0.638	(.027)***^a,b	0.561	(.031)***	0.494	(.043)***
STEM	.294	(.041)***^a,b	0.760	(.030)***^b	0.709	(.038)***	0.731	(.052)***
Health science	.001	(.065)^a,b	0.538	(.046)***^b	0.471	(.053)***^b	0.534	(.074)***
Social science, history, psychology, communication	−.002	(.051)^a,b	0.439	(.038)***	0.310	(.048)***	0.329	(.072)***
Education	−.361	(.086)***^a,b	0.210	(.057)**	0.185	(.057)**^b	0.216	(.073)**^b
Liberal arts/humanities, art, architecture	−.165	(.052)**^a,b	0.397	(.038)***^b	0.353	(.046)***	0.431	(.063)***^b
Others	.027	(.043)^a,b	0.498	(.030)***^a,b	0.387	(.035)***^b	0.323	(.052)***
Graduate degree
Business	.356	(.060)***^a,b	1.016	(.041)***^b	0.899	(.045)***^b	0.919	(.061)***
STEM	.250	(.071)***^a,b	0.896	(.048)***^b	0.853	(.049)***	0.924	(.060)***
Medicine, dentistry	−.253	(.098)**^a,b	1.336	(.061)***^a	1.484	(.063)***^a	1.715	(.084)***^b
Law	−.011	(.074)^a,b	0.932	(.050)***^b	0.828	(.053)***	0.968	(.071)***
Social science, history, psychology, communication	.045	(.093)^a,b	0.574	(.072)***^b	0.503	(.064)***^b	0.519	(.071)***
Education	−.005	(.077)^a,b	0.533	(.051)***^b	0.480	(.049)***^b	0.456	(.057)***^b
Liberal arts/humanities, art, architecture	−.404	(.088)***^a,b	0.445	(.061)***^b	0.458	(.065)***	0.499	(.083)***
Others	−.041	(.065)^a,b	0.773	(.044)***^b	0.698	(.046)***^b	0.631	(.059)***^b
Variance of the 15 estimated coefficients	0.049		0.083		0.102		0.139
Sample size	4,023		8,272		7,360		4,665
Pseudo R²	0.061		0.157		0.144		0.135

Note: Numbers in parentheses are standard errors. Estimates use Survey of Income and Program Participation weights adjusted for nonmatched respondents. All models control for race/ethnicity, birth year, region of birth, high school type, years since the highest degree, college preparation courses, math/science advanced placement courses, and year of survey. STEM = science, technology, engineering, and mathematics.

Indicates a statistically significant age difference compared to age 50s at alpha = .05.

Indicates a statistically significant gender difference at alpha = .05 compared to the corresponding coefficients in Table 3.

p < .05. **p < .01. ***p < .001 (two-tailed tests).

Table 3.

Median Regressions of 10-year Cumulative Log-transformed Earnings by Age Group, Women.

	Age 20s		Age 30s		Age 40s		Age 50s
Degree (Ref. = High School Graduate)	Coefficient	(SE)	Coefficient	(SE)	Coefficient	(SE)	Coefficient	(SE)
BA
Business	.691	(.056)***	0.832	(.053)***^a	0.686	(.060)***	.600	(.089)***
STEM	.966	(.088)***	0.990	(.092)***	0.825	(.123)***	.963	(.182)***
Health science	.705	(.068)***	0.838	(.060)***	0.669	(.065)***	.748	(.097)***
Social science, history, psychology, communication	.471	(.064)***	0.528	(.062)***	0.373	(.071)***	.346	(.096)**
Education	.271	(.069)***	0.299	(.057)***	0.390	(.052)***	.424	(.060)***
Liberal arts/humanities, art, architecture	.487	(.068)***^a	0.625	(.062)***^a	0.316	(.066)***^a	−.030	(.082)
Others	.616	(.060)***^a	0.757	(.055)***^a	0.613	(.061)***^a	.367	(.086)***
Graduate degree
Business	.993	(.096)***	1.312	(.085)***^a	1.205	(.098)***^a	.721	(.161)***
STEM	.859	(.122)***	1.281	(.115)***^a	0.855	(.125)***	.846	(.150)***
Medicine, dentistry	.531	(.162)**	1.564	(.138)***^a	1.327	(.146)***^a	.759	(.228)**
Law	.693	(.110)***	1.210	(.104)***	0.966	(.123)***	.886	(.200)***
Social science, history, psychology, communication	.590	(.107)***	0.861	(.090)***	0.805	(.085)***	.729	(.112)***
Education	.695	(.077)***	0.972	(.064)***^a	0.886	(.058)***	.773	(.068)***
Liberal arts/humanities, art, architecture	.537	(.120)***	0.699	(.110)***	0.656	(.104)***	.491	(.127)***
Others	.676	(.079)***^a	0.981	(.069)***	0.897	(.069)***	.946	(.087)***
Variance of the 15 estimated coefficients	0.036		0.108		0.082		0.074
Sample size	4,237		8,537		7,550		4,715
Pseudo R²	0.087		0.095		0.092		0.102

Indicates a statistically significant age difference compared to age 50s at alpha = .05.

p < .05. **p < .01. ***p < .001 (two-tailed tests).

Several findings stand out. First, we see significant differences in logged cumulative earnings by field of study over the life course. Among men and women with only a bachelor’s degree, STEM and business majors had the highest cumulative earnings over the life course. Among male graduate degree holders, business, STEM, medicine/dentistry, and law had the highest cumulative earnings from the 30s onward.

Second, effects of field of study on cumulative earnings differ by age and gender. In Tables 2 and 3, a superscript letter a indicates a statistically significant age difference at α = .05 compared to the coefficients for age 50s. A superscript letter b in Table 2 indicates a statistically significant difference between men’s coefficient and the corresponding coefficient for women in Table 3. In terms of age, the effects of field of study differ for men between the 20s and the prime working ages (i.e., ages 30 to 59). For example, among men with only a bachelor’s degree, majoring in education, was associated with 30 percent (= exp[–.361] – 1) lower cumulative logged earnings in the 20s compared to men who had only a high school diploma, but the effects become positive from age 30 to age 59. In contrast, STEM was associated with 34 percent higher cumulative logged earnings in the 20s relative to high school graduates. For advanced degree holders, a large negative effect on cumulative earnings from ages 20 to 29 is associated with some fields of study, indicating forgone earnings while in school. This implies that estimates of lifetime earnings that do not capture earnings early in life may overstate the net lifetime return to higher education. These results also support the notion that timing of school completion matters.

From ages 30 to 59, men with only a bachelor’s degree earn substantially more than high school graduates, but the extent varies sharply across field of study. In fact, the gaps in cumulative earnings between the highest- and lowest-earning fields of study among those with only a bachelor’s degree are much larger than the gap between high school graduates and bachelor’s degree holders.

We find a similar pattern among advanced degree holders. For some majors, such as law and medicine/dentistry, advanced degrees are associated with sharply higher earnings. In contrast to these majors, a graduate degree in liberal arts, humanities, arts, or architecture does not appear to raise earnings considerably relative to a bachelor’s degree in the same field. Another way to see if horizontal differentials change over age is to compare the variance of the 15 estimated coefficients in Table 2 by age groups: it steadily increased from .05 in the 20s to .08 in the 30s, .10 in the 40s, and then .14 in the 50s.

As Table 3 shows, we also find important variation by field of study for women. Among female bachelor degree holders, STEM, business, and health science are associated with the highest cumulative earnings relative to high school graduates over the life course. Advanced degrees in STEM, medicine/dentistry, and law are also associated with relatively high cumulative earnings among women, particularly in the 30s and 40s. As with men, effects of field of study tend to vary across different life stages for women, but the observed differences between the 20s and the prime working ages are not as sharp as for men. Instead, for women, many fields exhibit larger effects of higher education in the 30s than in other ages, compared to high school graduates. When the variances of the 15 estimated coefficients are computed, the highest differentials are observed in the 30s. These high differentials may be associated with the timing of childbearing and labor force status.

Several other differences by gender are notable. The overall net advantage of college education appears larger for women than for men regardless of field of study. Gender differences in the relative effects of field of study are most prominent in the 20s. Unlike men in their 20s, college-educated women earn more than comparable high school graduates regardless of field of study. During the prime working age, gender differences are not omnipresent, but we still see statistically significant differences in several fields. When there is a statistically significant difference, almost all (except bachelor’s degree in education, medicine, or dentistry in the 50s) show higher coefficients for women than for men.

Estimates of 40-year Lifetime Earnings by Field of Study

Using the regression results, we calculated 40-year lifetime earnings (ages 20 to 59). We adopted three different approaches. First, we report gross median lifetime earnings by field of study without taking into account any covariates. Second, we compute net 40-year lifetime earnings by field of study accounting for the covariates (using the estimates presented in Tables 2 and 3). We fix all covariates to the gender-specific grand mean of the entire sample. Thus, net lifetime earnings refer to the expected earnings net of all demographic and high school–related covariates. Third, we compute the net present value of lifetime earnings at age 20 to account for the time value of money, that is, the notion that future earnings are worth less than present earnings. The discount rate is a useful way to calculate the net present value of different fields of study in terms of 40-year earnings. How much people discount earnings far in the future relative to the opportunity costs of current investments in education, that is, their personal discount rate, depends on their own psychological disposition and the perceived riskiness of their investment. We apply a real discount rate of 4.0 percent, the average annual inflation rate over the past half century, which we assume might be the average psychological discount rate. Estimates based on alternative discount rates would yield different results.

Table 4 shows that a college degree is associated with sharply higher lifetime earnings for both men and women. A male high school graduate earns $1,425,000 over a 40-year work career (i.e., from age 20 to age 59), a male bachelor’s degree holder earns $2,209,000, and a man with an advanced degree earns $2,787,000, on average. Including covariates changes the 40-year lifetime earnings estimates for men to $1,490,000 for high school graduates, $2,149,000 for bachelor’s degree holders, and $2,641,000 for graduate degree holders. For women, a high school graduate earns $721,000, a bachelor’s degree holder earns $1,257,000, and a graduate degree holder earns $1,676,000, on average. When covariates are accounted for, the 40-year lifetime earnings become $728,000 for high school graduates, $1,114,000 for bachelor’s degree holders, and $1,470,000 for graduate degree holders. These latter figures are 49, 56, and 60 percent, respectively, of men’s lifetime earnings.⁷

Table 4.

Estimated Median 40-year Lifetime Earnings by Field of Study Using the Semisynthetic Cohort Method.

	Men			Women
Degree	(A) Gross Lifetime Earnings	(B) Net Lifetime Earnings	(C) Net Present Value at Age 20	(A) Gross Lifetime Earnings	(B) Net Lifetime Earnings	(C) Net Present Value at Age 20
High school graduate	$1,425,195	$1,489,656	$679,885	$721,108	$734,662	$339,328
BA
Business	$2,308,898	$2,255,050	$987,449	$1,442,296	$1,377,781	$631,985
STEM	$2,797,436	$2,663,359	$1,149,086	$1,811,564	$1,757,279	$800,417
Health science	$2,171,387	$2,109,116	$900,435	$1,518,012	$1,443,341	$652,117
Social science, history, psychology, communication	$1,946,944	$1,863,586	$818,671	$1,100,490	$1,050,886	$486,080
Education	$1,566,094	$1,535,131	$650,920	$1,029,495	$1,001,804	$441,623
Liberal arts/humanities, art, architecture	$1,889,523	$1,878,207	$795,301	$1,002,475	$977,333	$478,869
Others	$1,940,772	$1,925,376	$849,080	$1,281,613	$1,242,303	$583,706
Average	$2,208,771	$2,148,556	$933,236	$1,257,109	$1,210,476	$556,940
Graduate degree
Business	$3,027,151	$2,914,196	$1,238,332	$2,030,029	$1,893,831	$867,556
STEM	$3,020,020	$2,829,947	$1,182,468	$1,823,752	$1,738,367	$792,526
Medicine, dentistry	$5,563,507	$5,251,212	$1,962,536	$2,166,711	$2,118,263	$944,416
Law	$3,157,032	$2,902,453	$1,185,789	$1,822,950	$1,765,090	$781,409
Social science, history, psychology, communication	$2,110,262	$1,977,528	$845,792	$1,503,241	$1,394,857	$605,143
Education	$1,955,046	$1,858,827	$795,015	$1,591,857	$1,503,325	$657,305
Liberal arts/humanities, art, architecture	$1,821,236	$1,814,653	$740,359	$1,292,311	$1,193,295	$531,665
Others	$2,391,955	$2,268,759	$955,212	$1,719,616	$1,613,854	$690,683
Average	$2,786,890	$2,640,750	$1,092,931	$1,676,073	$1,579,473	$694,259

Note: We use a semisynthetic cohort method to estimate lifetime earnings. Gross earnings are based on descriptive statistics without controlling for any covariates. For the estimates of net lifetime earnings and its present value, race/ethnicity, birth year, region of birth, high school type, years since the highest degree, college preparation courses, math/science advanced placement courses, and year of the survey are controlled for in median regression models. The present value is calculated at age 20 using a real discount rate of 4 percent.

However, differences by level of education obscure sizeable variation by field of study. For men, the gap in cumulative 40-year earnings between a bachelor’s degree in education and a high school diploma is particularly narrow, such that after controlling for covariates (column B), the lifetime earnings advantage of a bachelor’s in education compared to being a high school graduate is $45,000 (= $1,535,000 − $1,490,000). In contrast, the 40-year earnings gain associated with a bachelor’s degree in STEM is $1,173,000 (= $2,663,000 − $1,490,000). Put differently, the gap in cumulative 40-year earnings between a bachelor’s degree in STEM versus education is 26 times larger than the gap between a bachelor’s degree in education and a high school diploma. For female workers, lifetime earnings gaps across fields of study are also quantitatively meaningful, but they are smaller than the gaps for men.

Are the observed lifetime earnings differences across fields of study statistically significant? To illustrate, Figure 1 displays the 95 percent confidence interval of the difference in 40-year net log-transformed lifetime earnings by field of study relative to high school graduates. These estimates confirm statistically significant differentials by field of study, even though our sample size for each field is rather small. An asterisk in the female graph (Panel B) indicates that the female education premium is statistically higher than the male education premium.

Figure 1.

Difference in 40-year net log lifetime earnings from high school graduates.

Among graduate degree holders, lifetime earnings also sharply vary by field of study. On average, the 40-year lifetime earnings gain associated with an advanced degree compared to a bachelor’s degree is $492,000 for men, after making adjustments for the covariates. However, much of this advantage is driven by the relatively high returns to law, business, and medical majors. For other fields, the lifetime financial returns of an advanced degree are more modest. For example, among social science majors, the lifetime return to graduate school is around $114,000 relative to a bachelor’s degree only in that field. Surprisingly, for liberal arts, humanities, arts, and architecture majors, the lifetime financial return to graduate education compared to a bachelor’s degree in the same major is negative (i.e., $1,878,000 for a bachelor’s degree vs. $1,821,000 for a graduate degree). This is mainly because the earnings of individuals who earn advanced degrees in these fields are much lower than others in their 20s and are still negative in the 30s. In regard to this pattern, however, we caution that the composition of detailed fields in liberal arts, humanities, arts, and architecture majors may differ between bachelor’s and graduate degree holders, and the financial return to graduate education often continues to grow after age 60 (Tamborini et al. 2015). Nevertheless, our results do call attention to concerns about the long-term financial return to graduate study in these fields.

Compared to men, women tend to garner more relative total financial returns from an advanced degree (L. Morgan 2008), although this finding is statistically insignificant, due mainly to the small sample sizes of female graduate degree holders. For example, a female graduate degree holder in a social science earns $344,000 more than a woman with a bachelor’s degree in the same major, whereas the gap between a bachelor’s and graduate degree is only $114,000 for men. Surprisingly, female graduate degree holders in education enjoy $502,000 additional lifetime earnings compared to women with a bachelor’s degree in education, and the difference is statistically significant. Such gains are partially because an advanced degree not only raises women’s annual salaries and occupational prospects, but it also increases the likelihood of greater participation in the labor force over the life course. Indeed, our analyses (not shown here) show that the proportions of zero earnings for most undergraduate majors are higher than those for graduate degree holders, at least in their 30s.

To calculate the net present value, we apply a 4 percent real discount rate. As expected, the 40-year earnings differentials between college degree holders and their high school graduate counterparts narrow when using discounted earnings streams. At the same time, substantial variation across field of study remains. For example, the present value of an advanced degree in liberal arts, humanities, arts, and architecture at age 20, compared to a high school diploma, is $60,000 for men. Surprisingly, the present value of a bachelor’s degree in education at age 20 is negative compared to a high school diploma ($680,000 for a high school diploma vs. $651,000 for a bachelor’s degree in education).

Discussion and Conclusions

We investigated the relationship between field of study and lifetime earnings using a nationally representative sample of SIPP respondents matched to longitudinal earnings records in administrative data. Given the few prior studies on this topic, our primary goal was to generate baseline estimates that would extend our knowledge about the size of lifetime earnings differentials across field of study and how these differentials vary across age and by gender. Assessing these issues helps advance our understanding of the long-term effects of horizontal stratification in higher education on financial rewards in the labor market, earnings inequality, and life chances.

Our results point toward several conclusions. Overall, we provide new evidence that for college graduates, field of study constitutes a critically important source of lifetime earnings inequality. Our estimates indicate that horizontal stratification in education across field of study may now be more consequential than vertical stratification for long-term rewards in the labor market. That is, college graduates’ lifetime earnings (i.e., ages 20 to 59) exhibit gaps by field of study that are larger in many instances than the gaps between college and high school graduates. For example, a bachelor’s degree in social science among men is associated with a lifetime earnings gain of $374,000 compared to a high school diploma only, and an advanced degree in social science yields an additional $114,000. However, majoring in STEM instead of social science is associated with much larger gains. Even without obtaining a graduate degree, a bachelor’s degree in STEM is associated with $800,000 higher lifetime earnings compared to a bachelor’s in social science. Statistically significant differentials across fields of study were also evident among male advanced degree holders and female bachelor’s degree holders. This suggests that long-term earnings inequality within educational levels may be more important than previously thought. It also suggests that the study of earnings disparities by educational attainment should be expanded to include other dimensions, such as field of study.

Our estimates of lifetime earnings across different fields of study, in terms of relative ranking, are fairly consistent with published estimates in previous reports using the ACS, to the extent that our field-of-study categories overlap. There are, however, some notable differences. In fact, estimates in two previous reports using the ACS (i.e., Herschbein and Kearney 2014; Julian 2012) are far from consistent. Julian (2012) reports 40-year lifetime earnings of $1.8 million to $3.5 million, depending on college major, among individuals whose highest degree is a bachelor’s, whereas Herschbein and Kearney (2014) report a range of $800,000 to $2.1 million for the same 40-year span. Our findings are in between those estimates. The substantial discrepancies between these two prior estimates are mainly due to the different sample selection criteria. Julian used full-time, full-year employed respondents only, whereas Herschbein and Kearney included anyone who worked for at least one week within the past 12 months. The former may overstate lifetime earnings; the latter may understate it. Our estimates of lifetime earnings by field of study, based on long-term longitudinal information on earnings, are the first that do not hinge on strong labor force status assumptions.

Our findings also provide new evidence that field of study can have age-differentiated effects on the careers of men and women. Doctors, dentists, and lawyers, for example, exhibit distinct life course patterns in earnings, such as relatively steep earnings growth at later career stages. If earnings growth evolves differently over the life course by field of study, then horizontal dimensions of education stratification may be increasingly important for the sequencing of wealth formation, social mobility, and, ultimately, retirement savings and income in later life. Through direct and indirect pathways, field of study may also influence the timing of life transitions, including withdrawal from the labor force and the pace of health decline in old age. These results demonstrate the usefulness of a life course perspective when examining returns to education by field of study.

Several findings relating to gender are notable. We found significant gender differences in the association between field of study and earnings by age. For men in many fields, the cumulative returns to college education past one’s 20s were negative, but returns were positive for women regardless of field of study. This is partly due to the fact that highly educated men are less likely to be in the labor force than are high school graduates in their early 20s, whereas highly educated women are more likely than their less-educated counterparts to be in the labor force. Although not conclusive, this implies that short-term opportunity costs of a college education are substantially higher for men than for women.

Our findings also show greater relative long-run returns to higher education across most fields of study for women than for men. Despite small sample sizes, half of the observed gender differences across fields of study are statistically significant, and most gaps are substantial. It may be noteworthy that education and health majors, fields in which women are concentrated, show statistically significant gender differences. For example, a bachelor’s degree in education is associated with an additional $140,900 over a lifetime for men but an extra $308,400 for women relative to their high school–graduate counterparts. In part, this is because traditionally female concentrated fields, such as education, provide a higher likelihood of staying in labor markets for women, and thus the lifetime monetary value of these fields for women is not as low as that measured using annual earnings or hourly wages. Along with opportunity costs in the 20s discussed earlier, this larger relative long-run return to higher education might be why women are more likely than men to go to college. Further research on this possibility is warranted.

Finally, our findings shed light on the highly relevant question of whether a college education is worth its monetary cost. On average, our estimates indicate that the lifetime return to college clearly offsets the initial investment for men and women, no matter a person’s field of study. However, the extent of the payoff depends on one’s field of study and gender. With the average price of tuition and required fees of a four-year college rising to about $12,967 per year in 2010 (National Center for Education Statistics 2012), the total average cost is about $52,000. Applying a 4 percent real discount rate, the net present value of different college degrees varied from 2 to 23 times higher than $52,000 for most major fields. In some instances, however, the value of a bachelor’s degree (i.e., education) or an advanced degree (i.e., liberal arts, humanities, arts, and architecture) fell short of the $52,000 threshold for men. Yet, our measure of lifetime earnings does not account for total compensation, including pensions, health insurance, and other benefits. Because jobs requiring a college degree are more likely to offer generous benefits (e.g., secondary-school teachers), using total compensation would alter the net present value of some fields of study relative to a high school diploma. Furthermore, the total benefit of education is not limited to its direct pecuniary gain (Hout 2012; Oreopoulos and Salvanes 2011).

Several limitations are noteworthy. First, due to smaller sample sizes among older respondents, we limited the upper age range to 59. To the extent that labor force participation and earnings after age 59 differ by educational level or field of study, our lifetime estimates could be somewhat altered. Second, we do not measure the entire lifetime earnings of a single birth cohort, because the longitudinal earnings data do not extend further back than 1982. We reiterate that estimates of real cohort lifetime earnings may yield different results than those based on the semisynthetic cohort method. Furthermore, the relationship between field of study and labor market outcomes may differ for future cohorts. Lower-educated groups were more negatively affected by the recent economic downturn than were higher-educated groups (Sum and Khatiwada 2010), and unemployment can be a life-changing event that has long-run costs for affected workers’ earnings (Couch et al. 2013). Third, because of sample sizes, we could not estimate the earnings of master’s, professional, and doctoral degree holders separately. The share of master’s degrees and more advanced degrees varies by field, so the implications of our estimates for advanced degrees may differ by fields. Fourth, although college quality is beyond this study’s scope, and no information on this is available in our data, it represents another important dimension of horizontal stratification (Ma and Savas 2014; Zhang 2005). Finally, as a cautionary note, these results do not reflect causal impacts of field of study. Unobserved individual heterogeneity (e.g., reflecting variation in ambition, intelligence, creativity, and preferences for income versus leisure or intrinsic job rewards) may contribute to the relationships between field of study and earnings observed here.

Nonetheless, this study provides new evidence that highlights substantial differences in the lifetime-earnings returns to education by field of study. These results represent an important insight into the critical, and perhaps increasing, role of a horizontal dimension in educational stratification in shaping life course patterns in the labor market and the accumulation of earnings over a lifetime. We close our discussion by noting additional directions for research. The labor market mechanisms that generate earnings differences by field of study are worth exploring further. For example, research on the effect of occupational sorting by field of study is warranted.⁸ Many sociologists implicitly equate occupation with lifetime earnings (Blau and Duncan 1967; Hauser and Warren 1997; Wilkinson 1966), particularly, researchers studying intergenerational social mobility (Featherman and Hauser 1978). This association has never been systematically evaluated. While field of study has been theorized as a dimension of horizontal stratification, occupational sorting has been considered a process of vertical stratification. How to resolve this contradiction in social stratification theory remains unresolved. Several recent studies highlight the links between level of education, field of study, and occupation (e.g., Morgan et al. 2013; Reynolds et al. 2006; Roksa and Levey 2010), but more studies on how lifetime earnings are differentiated jointly by field of study and occupation would be beneficial.

Our analysis also has important implications for the study of intergenerational mobility. Despite the widely shared belief that an increase in earnings inequality lowers intergenerational income mobility (Corak 2013), and concerns about the rising influence of family background on the probability of obtaining higher education (Reardon 2011), other evidence suggests that intergenerational earnings mobility may not have declined significantly in recent decades (Chetty et al. 2014). Large earnings differentials across field of study may represent an unacknowledged channel of upward mobility (Ma and Savas 2014). Evidence suggests that students with low household socioeconomic resources are more likely to choose more financially rewarding areas, such as business or engineering, over less financially rewarding liberal arts majors (Goyette and Mullen 2006). In regard to racial differentials in earnings, field of study may help explain the apparently high upward mobility of Asian Americans (Sakamoto, Goyette, and Kim 2009) and the lack of mobility among recent cohorts of African Americans (Bloome and Western 2011). This topic merits further investigation in future research.

Our results also suggest the possibility that the increase in the college premium over recent decades is concentrated in a small number of fields rather than being universal. A clear avenue for future research to consider is the extent to which the returns to education over historical time vary by field of study. Differential earnings growth rates over the life course by field of study (following the same individuals over time) is also worthy of consideration. As the population ages and a college degree becomes more important, topics of particular policy relevance include how field of study can have long-term consequences on outcomes, such as job stability, wealth formation, retirement behavior, and health.

Footnotes

Acknowledgements

We thank Rob Warren and four anonymous reviewers for helpful comments. Thanks also to Gayle Reznik, Patrick Purcell, Kevin Whitman, and Natalie Lu for comments. All remaining errors are our own.

Research Ethics

Our research protocol was reviewed and approved by the University of Kansas Institutional Review Board. Access to SSA data linked to U.S. Census Bureau survey data is subject to restrictions imposed by Title 13 of the U.S. Code. The data are accessible only at a secured site. All data analyses were conducted by a researcher who maintains a Special Sworn Status. All statistical results were reviewed by the disclosure review committee at the Social Security Administration before their release.

Authors’ Note

The views expressed in this paper are those of the authors and do not represent the views of the Social Security Administration. For researchers with access to these data, the computer programs used in this analysis are available upon request.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institute of Health (1R03HD073464) and the Spencer Foundation (201400077).

Supplemental material

The online appendices are available at .

Notes

Author Biographies

ChangHwan Kim is an associate professor of sociology at the University of Kansas. His areas of interests include labor-market inequality, stratification, education, and Asian Americans studies. Teaming up with Christopher R. Tamborini and Arthur Sakamoto, he is currently doing research on inequality in longterm earnings. His recent work has appeared, among other outlets, in Demography, Social Forces, and Social Problems.

Christopher R. Tamborini is a senior researcher in the Office of Retirement Policy at the U.S. Social Security Administration. He is also a Lecturer in the Department of Sociology at the Catholic University of America in Washington, DC and has a research affiliation with the Institute for Policy & Social Research at the University of Kansas. He received his PhD in sociology from the University of Texas at Austin in 2005. A central concern of his research is to understand the processes that shape social stratification in long-term patterns over the life course. His recent work has appeared in Demography, Journal of Marriage and Family, and Social Forces.

Arthur Sakamoto is professor of sociology at Texas A&M University. His areas of research interest include social stratification and inequality, racial and ethnic relations, economic sociology, and Japanese studies.

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