Skip to main content
NCBI home page
HHS Author Manuscripts logoLink to HHS Author Manuscripts
. Author manuscript; available in PMC: 2024 Nov 14.
Published in final edited form as: J Vocat Rehabil. 2023 Nov 14;59(3):263–272. doi: 10.3233/jvr-230044

College Degree Majors and Associated Earnings: Are There Differences Between People with Visual Impairments and the General Population?

Michele C McDonnall 1,*, Jennifer L Cmar 1, Zhen McKnight 1
PMCID: PMC10961939  NIHMSID: NIHMS1951373  PMID: 38528915

Abstract

BACKGROUND:

College graduates without visual impairments earn more than college graduates with visual impairments. Differences in degree majors obtained or differences in earnings associated with degree majors for the two groups might explain this discrepancy in earnings.

OBJECTIVE:

The purpose of this study was to examine (a) differences in degree major categories and (b) relationships between degree majors and earnings for college graduates with and without visual impairments.

METHOD:

We obtained data for college-educated, working-age adults from the American Community Survey. We utilized descriptive statistics to compare degree major categories by visual impairment and multiple regression to evaluate predictors of annual earnings for college graduates who worked full-time/full-year.

RESULTS:

Small differences in degree majors obtained were found between college graduates with and without visual impairments. Significant predictors of earnings included 23 out of 25 degree majors and several other demographic and socioeconomic characteristics. Effects of three majors and several other variables differed for people with visual impairments.

CONCLUSION:

People with visual impairments were slightly more likely to hold degrees with lower-paying majors and less likely to hold degrees with two higher-paying majors. Regardless of degree major, college graduates with visual impairments had substantially lower average earnings than the general population.

Keywords: blind, low vision, visual impairment, postsecondary education, college degree major, earnings

People who are blind or have low vision (i.e., those with visual impairments) are much less likely to be employed than the general population (McDonnall & Sui, 2019), and many of those who work earn less than a living wage (McDonnall et al., 2021). Employment is associated with several benefits (Modini et al., 2016; van der Noordt et al., 2014), but earnings are also important. Lack of sufficient income is associated with substantial stress (PwC, 2016, 2022) and does not allow one to live comfortably. Financial security, on the other hand, is associated with many positive outcomes, such as better health, social connectedness, and happiness, and is considered a key component of overall well-being (Cruwys et al., 2019; Netemeyer et al., 2018). Sufficient earnings are also essential to allow one to contribute to retirement savings, which are lacking for the majority of the U.S. population (Dickler, 2022). Unfortunately, people with disabilities, including those with visual impairments, are much less likely to have sufficient income than people without disabilities (Crudden et al., 2022; Paul et al., 2021).

Education level is associated with earnings, and its impact on earnings is intensified when one considers not just current earnings but lifetime earnings (Tamborini et al., 2015). People with a bachelor’s degree earn substantially more than people with lower levels of education (referred to as the “college earnings premium”; Eckstein & Nagypál, 2004), and this holds true for people with visual impairments (McDonnall et al., 2022). Yet people with visual impairments who work earn substantially less than the general population (defined in this study as people without visual impairments), even when accounting for education level (McDonnall et al., 2022).

Possessing a 4-year college degree is strongly associated with being employed for people with visual impairments (Lund & Cmar, 2019a, 2019b; McDonnall & Tatch, 2021) as well as being associated with greater earnings (McDonnall et al., 2022). Clearly, obtaining a college degree is valuable for people with visual impairments, potentially more so for this population than for the general population given its strong association with employment outcomes. Yet a much smaller percentage of people with visual impairment of all ages have a college degree than those without disabilities (McDonnall & Tatch, 2021).

The college earnings premium differs noticeably by degree major: graduates with certain degree majors earn substantially more, on average, than graduates with other majors (Bankrate, 2021; Carnevale et al., 2015). This topic has received considerable attention in the economics literature (Altonji et al., 2014; Angle & Wissmann, 1981; Arcidiacono, 2004; Kirkeboen et al., 2016; Light & Schreiner, 2019; Webber, 2014, 2016). A thorough analysis of return on investment for a bachelor’s degree determined that both degree major and institution had an impact on return on investment, but that degree major mattered more than institution attended (Cooper, 2021). The study also found that some majors can have a negative return on investment, meaning that the cost of obtaining the college degree outpaces the financial benefit in terms of higher earnings obtained because of the degree. In fact, 37% of degree major programs at specific institutions had a neutral or negative return on investment (Cooper, 2021). Undoubtedly, the choice of degree major can have a big impact on earnings for college graduates.

In addition to education level, other demographic factors known to be associated with earnings for the general population are gender (men earn more than women), race (Whites earn more than minorities, with the exception of Asians), and age (earnings increase with age to a point, then begin to decrease; Bureau of Labor Statistics, 2022). Few studies have investigated predictors of earnings for people with visual impairments: only three studies were identified that utilized earnings as an outcome variable (Bell & Mino, 2013; Capella, 2001; Estrada-Hernández, 2008). Two studies were conducted with annual vocational rehabilitation case service (RSA-911) data and utilized multiple regression to identify predictors of earnings. The first documented that younger age, higher education level, and a greater amount of money expended on the case were significantly and substantively associated with higher earnings, while agency type, number of services received, and months receiving services were not (Capella, 2001). The second study was a replication of the first and had similar findings, but it included two additional demographic variables: male gender was significantly and substantively associated with higher earnings whereas race was not (Estrada-Hernández, 2008). The final study that investigated correlates of earnings utilized univariate analyses with a survey sample and found that male gender, higher education level, and being a braille reader were associated with greater earnings, while age, race, and level of vision loss were not associated with earnings (Bell & Mino, 2013).

Although we are aware of some factors that are associated with earnings for people with visual impairments, many factors have likely not been identified or investigated. For example, we can expect degree major to impact earnings for college graduates with visual impairments as it does for the general population. However, we do not know if college graduates with visual impairments have similar degree majors as the general population. Perhaps differences in degree majors selected is a relevant factor that explains some of the discrepancy in earnings between college graduates with visual impairments and the general population of college graduates. A small amount of research has been conducted regarding degree majors of college students with visual impairments.

Data from the mid-1990s indicate that undergraduates with visual impairments in the United States differed in their degree majors compared to undergraduates without disabilities (Horn & Berktold, 1999). A smaller percentage of students with visual impairments pursued degrees in engineering, the humanities, and health, while a higher percentage pursued degrees in education and computer/information science. Also using data from the mid-1990s, Richardson and Roy (2002) found that college students with visual impairments in the United Kingdom pursued different degree majors than the general population: fewer sought degrees in the medical or medical-related fields, veterinary science, education, agriculture, architecture, and mathematical science, and more sought degrees in physical sciences, computer science, social studies, and the humanities. Both studies focused on college students, not college graduates; therefore, some of the students likely did not graduate with the degree or major they were pursuing. The Kessler Foundation conducted a study of recent college graduates with disabilities (including some with visual impairments) and without disabilities. Recent graduates with disabilities were less likely to major in business or engineering, and more likely to major in psychology, communications, and visual and performing arts compared to graduates without disabilities (Phillips et al., 2022). Although this study is more current and included data from college graduates rather than students, the article did not provide results by disability type.

We could not locate any research studies that specifically focused on earnings of visually impaired college graduates. We know that earnings of college graduates with visual impairments are lower than earnings for the general population of college graduates (McDonnall et al., 2022), but we do not know the reasons for this earnings differential. One potential factor is college degree major. Although we assume that degree major is associated with earnings for college graduates with visual impairments, we do not know how the relationships between earnings and degree major compare to relationships found in the general population. Do college graduates with visual impairments possess different majors than the general population, and, if so, do these differences explain some of the earnings gap? Do majors that afford significantly higher earnings for the general population hold the same premium for people with visual impairments? These are the types of queries that guided this research study. The specific research questions investigated were:

  1. What are the most common degree major categories for college graduates with visual impairments, and how do their major categories compare to the majors of the general population?

  2. Are the relationships between specific degree majors and earnings the same for college graduates with visual impairments as for the general population of college graduates?

  3. Are the relationships between other factors associated with earnings (i.e., demographic and socioeconomic characteristics) the same for college graduates with visual impairments as for the general population of college graduates?

Method

Data Source and Sample

The person-level dataset from the 2015–2019 American Community Survey (ACS) 5-Year Public Use Microdata Sample (PUMS) was utilized for this study. The ACS, administrated by the Census Bureau, aims to provide nationwide information about the social, economic, housing, and demographic characteristics of the U.S. population (U.S. Census Bureau, 2020). ACS data are collected throughout each year from approximately 3.5 million households and are pooled across the calendar year to provide annual estimates. The 1-year ACS PUMS files contain de-identified records for approximately two-thirds of the ACS sample for a given year (U.S. Census Bureau, 2021c). The Census Bureau combines five 1-year PUMS files to create the 5-year PUMS, which includes data for about 5% of the U.S. population.

The sample for Research Question 1 included 2,989,490 individuals who had a bachelor’s degree and were between 21 and 64 years old (weighted N = 60,091,069). For Research Questions 2 and 3, we further restricted the sample to individuals who worked full-time/full-year, defined as working 35 hours or more per week and 50 to 52 weeks per year (Ryan, 2012). We also excluded individuals whose earnings were in the top 1% and bottom 1% of the sample to minimize severe outliers at both ends of the distribution, resulting in a sample size of 1,919,635 for the earnings analysis (weighted N = 39,140,762).

Dependent Variable

The dependent variable was annual earnings, defined as total personal income (in U.S. dollars) from wages, salary, and self-employment in the previous 12 months. Wages or salary income included wages, salary, commission, bonuses, and tips from all jobs before deductions for taxes, whereas self-employment income included individuals’ net income from their own nonfarm or farm businesses after business expenses. We applied the inflation adjustment factor in the 2015–2019 ACS 5-Year PUMS to adjust all earnings to reflect 2018 dollars, as recommended when working with this dataset (U.S. Census Bureau, 2021a).

Independent Variables

Demographic and Socioeconomic Characteristics

The primary variable of interest was visual impairment. This variable was dichotomously coded (0 = no, 1 = yes) according to individuals’ responses to the ACS question “Is this person blind or does he/she have serious difficulty seeing even when wearing glasses?” Demographic and socioeconomic variables associated with earnings were also included in the analysis. Age was a continuous variable and was grand-mean centered based on the average age of the full-time/full-year college graduate sample. Gender was dichotomously coded (0 = male, 1 = female). We used two indicator variables for race: Black race and other races (0 = no, 1 = yes); White was the reference group. Ethnicity was dichotomously coded (0 = not Spanish/Hispanic/Latino, 1 = Spanish/Hispanic/Latino). Other disabilities (0 = no, 1 = yes) indicated the presence of one or more disabilities (i.e., hearing, cognitive, ambulatory, self-care, or independent living difficulties) other than visual impairment. If a person had two bachelor’s degrees, double major was coded 1; otherwise, it was coded 0. If a person had a master’s degree or above, advanced degree was coded 1; otherwise, it was coded 0. Social Security Disability Insurance (SSDI) receipt indicated whether an individual received Social Security benefits any time within the past 12 months (0 = no, 1 = yes). The ACS dataset does not differentiate between SSDI and early Social Security retirement benefits; therefore, it is possible but unlikely that some 62 to 64-year-old full-time/full-year workers in our sample received early retirement benefits rather than SSDI. Class of worker indicates the type of ownership of the organization for which a person works (U.S. Census Bureau, 2021b). Three indicators of class of worker included private non-profit, government, and self-employment; private for-profit was the reference group. State-level median annual earnings, based on data released by the U.S. Bureau of Labor Statistics in May 2019, was a continuous control variable, included to account for differences in earnings due to state-level variation.

Degree Majors

The ACS questionnaire includes an item about participants’ bachelor’s degrees: “Please print below the specific major(s) of any BACHELOR’S DEGREES this person has received. (For example: chemical engineering, elementary teacher education, organizational psychology).” ACS staff classified the written responses into 1 of 173 specific degree majors, for up to two majors per person. We grouped the 173 specific degree majors into 13 broad degree major categories based on the ACS 2019 Code List (U.S. Census Bureau, n.d.) for Research Question 1. We identified the 25 most common specific degree majors for all people between ages 21 and 64 with visual impairments who had a bachelor’s degree for Research Question 2. The top 25 degree majors accounted for 63.1% of all majors held by college graduates with visual impairments. We created 25 dichotomous variables indicating whether a person had each of the most common degree majors (0 = no, 1 = yes), which allowed us to compare earnings for each major to earnings for all other majors.

Data Analysis

To answer Research Question 1, we computed relative frequencies of bachelor’s degrees received by each major category and identified the most common degree major categories for all working-age college graduates with and without visual impairment. To address Research Questions 2 and 3, we used multiple regression to investigate factors associated with annual earnings for college graduates who worked full-time/full-year. We used the following model-building process for the multiple regression analysis. First, we ran a model that included demographic and socioeconomic characteristics and the 25 degree majors. Then, we added interactions between visual impairment and each independent variable. Last, we removed all interactions with p > .10 to create the final model. We utilized SAS 9.4 PROC SURVEYMEANS and PROC SURVEYFREQ for descriptive statistics and PROC SURVEYREG for regression analyses. Personal weights from the 2015–2019 ACS 5-year PUMS file were applied in all analyses to obtain nationally representative estimates and adjusted standard errors.

Results

Table 1 presents degree major categories for all working-age college graduates with and without visual impairments. The four most common degree major categories for both groups were (a) Business, (b) Arts & Humanities, (c) Education, and (d) Science & Engineering Related Fields. Compared to the general population of college graduates, a smaller percentage of graduates with visual impairments had degrees in the Engineering and Biological, Agricultural, & Environmental Sciences categories. A larger percentage of college graduates with visual impairments had degrees in Education and Consumer Services & Recreation than did graduates without visual impairments.

Table 1.

Degree Major Categories for College Graduates With and Without Visual Impairments (VI)

With VI Without VI
Degree major category n % n %
Business 136,592 20.9 13,750,000 20.9
Arts & Humanities 86,581 13.2 8,584,146 13.1
Education 75,765 11.6 6,226,249 9.5
Science & Engineering Related Fields 61,838 9.4 6,039,285 9.2
Consumer Services & Recreation 52,817 8.1 4,123,433 6.3
Social Science 50,664 7.7 5,277,126 8.0
Psychology 38,093 5.8 3,304,317 5.0
Engineering 35,640 5.4 4,952,731 7.5
Biological, Agricultural, & Environmental Sciences 34,896 5.3 4,419,793 6.7
Computers, Mathematics & Statistics 30,733 4.7 3,390,648 5.2
Communications 27,728 4.2 3,115,524 4.7
Physical & Related Science 18,195 2.8 1,953,694 3.0
Multidisciplinary Studies 5,699 0.9 579,095 0.9
Open in a new tab

Note. Weighted N = 60,091,069 people and 66,374,116 degree majors. Data from the 2015–2019 American Community Survey 5-year Public Use Microdata Sample. Estimates are weighted to be nationally representative.

Table 2 contains descriptive information about the demographic and socioeconomic characteristics of college graduates with and without visual impairments who worked full-time/full-year. Average annual earnings were $93,219 (95% CI [93,097.71, 93,339.76]) for all college graduates who worked full-time/full-year. Graduates with visual impairments earned 87.3% (M = $81,433, 95% CI [80,072.67, 82,794.03]) of what graduates without visual impairments earned (M = $93,307, 95% CI [93,185.91, 93,427.34]).

Table 2.

Descriptive Statistics for College Graduates Who Worked Full-time/Full-year

With VI Without VI
Variable n % n %
Gender
 Male 143,940 49.7 20,281,490 52.2
 Female 145,789 50.3 18,569,543 47.8
Race
 White 212,485 73.3 29,675,832 76.4
 Black 38,542 13.3 3,489,817 9.0
 Other 38,702 13.4 5,685,384 14.6
Hispanic ethnicity 34,064 11.8 3,381,557 8.7
Other disabilities 74,010 25.5 890,823 2.3
SSDI receipt 2,777 1.0 105,900 0.3
Double major 30,735 10.6 4,057,346 10.4
Advanced degree 103,132 35.6 14,202,985 36.6
Class of worker
 Private for-profit 155,524 53.7 23,006,223 59.2
 Private non-profit 38,028 13.1 4,679,936 12.0
 Government 74,435 25.7 8,428,274 21.7
 Self-employment 21,742 7.5 2,736,600 7.0
Agea 45.28 0.13 41.89 0.01
Open in a new tab

Note. Weighted N = 39,140,762. Data from the 2015–2019 American Community Survey 5-year Public Use Microdata Sample. Estimates are weighted to be nationally representative. VI = visual impairment; SSDI = Social Security Disability Insurance.

a

Values signify means and standard errors.

Table 3 provides results of the multiple regression analysis predicting annual earnings of college graduates based on degree major and other factors. The independent variables in the model explained 17.3% of the variance in earnings. When controlling for demographic and socioeconomic variables and degree majors, college graduates with visual impairments had substantially lower earnings than the general population of college graduates. According to the model estimates, college graduates with visual impairments earned 78.2% of what college graduates without visual impairments earned. Of the 25 degree majors included in the model, 23 were significant predictors of earnings for all college graduates: 12 majors were associated with higher earnings, and 11 were associated with lower earnings. The only two majors that did not significantly predict earnings were Business Management & Administration and Communications. People with visual impairments who had Computer Science or Accounting degrees had significantly lower earnings than the general population with those degrees, although they still earned more on average than people with other majors. People with visual impairments who had a General Education degree had significantly higher earnings than the general population who held those degrees, although they still earned less on average than people with other majors.

Table 3.

Results of Multiple Regression Predicting Annual Earnings of College Graduates

Variable B SE B β t p
Intercept 62,559.68 530.40 0 117.95 <.001
Visual impairment (VI) −13,628.65 1,201.50 −.01 −11.34 <.001
Age 1,526.07 5.67 .22 269.13 <.001
Age x VI −473.96 50.93 −.01 −9.31 <.001
Female gender −23,610.68 138.72 −.15 −170.20 <.001
Female gender x VI 5,373.77 1,275.38 .00 4.21 <.001
Race
 Black −18,779.79 202.93 −.07 −92.54 <.001
 Black x VI 2,329.09 1,341.92 .00 1.74 .087
 Other −4,980.06 182.32 −.02 −27.32 <.001
Hispanic ethnicity −13,644.38 225.27 −.05 −60.57 <.001
Other disabilities −15,751.18 381.45 −.03 −41.29 <.001
Other disabilities x VI 8,161.51 1,512.76 .00 5.40 <.001
SSDI receipt −28,765.88 1,181.22 −.02 −24.35 <.001
SSDI receipt x VI 14,057.15 7,604.04 .00 1.85 .068
Double major 175.21 208.82 .00 0.84 .404
Advanced degree 29,661.94 154.35 .18 192.18 <.001
Advanced degree x VI −8,411.44 1,640.26 −.01 −5.13 <.001
Class of worker
 Private non-profit −18,507.73 192.21 −.08 −96.29 <.001
 Private non-profit x VI 5,619.12 1,843.66 .00 3.05 .003
 Government −24,251.70 141.98 −.13 −170.81 <.001
 Government x VI 12,632.73 1,353.06 .01 9.34 <.001
 Self-employment 2,657.98 341.82 .01 7.78 <.001
Median state earnings 1.02 0.01 .06 81.78 <.001
Degree majora
 Business Management & Administration 386.02 230.05 .00 1.68 .097
 General Business 4,343.20 319.34 .01 13.60 <.001
 Psychology −4,710.38 260.26 −.01 −18.10 <.001
 Nursing 8,251.78 247.56 .02 33.33 <.001
 Accounting 9,508.29 332.44 .02 28.60 <.001
 Accounting x VI −5,957.05 2,600.23 .00 −2.29 .025
 General Education −16,780.31 261.66 −.04 −64.13 <.001
 General Education x VI 6,006.70 2,878.13 .00 2.09 .040
 Political Science & Government 12,531.13 512.20 .03 24.47 <.001
 Biology 22,358.71 399.18 .05 56.01 <.001
 Criminal Justice & Fire Protection −3,760.57 283.51 −.01 −13.26 <.001
 Computer Science 15,699.27 393.39 .03 39.91 <.001
 Computer Science x VI −6,531.33 2,932.96 .00 −2.23 .029
 Communications 346.50 336.09 .00 1.03 .306
 Elementary Education −16,686.09 248.78 −.03 −67.07 <.001
 Marketing & Marketing Research 7,364.40 339.84 .02 21.67 <.001
 Finance 20,652.06 454.35 .04 45.45 <.001
 English Language & Literature −4,212.13 389.59 −.01 −10.81 <.001
 History −2,623.16 443.17 .00 −5.92 <.001
 Sociology −4,305.68 407.40 −.01 −10.57 <.001
 Social Work −16,984.91 375.57 −.02 −45.22 <.001
 Economics 22,443.00 517.67 .04 43.35 <.001
 Electrical Engineering 19,328.74 530.58 .04 36.43 <.001
 Liberal Arts −6,742.00 506.79 −.01 −13.30 <.001
 General Engineering 5,939.27 649.57 .01 9.14 <.001
 Fine Arts −16,947.44 542.77 −.02 −31.22 <.001
 Mathematics 7,405.86 625.77 .01 11.83 <.001
 Commercial Art & Graphic Design −8,575.46 380.93 −.01 −22.51 <.001
Open in a new tab

Note. Weighted N = 39,140,762. Data from the 2015–2019 American Community Survey 5-year Public Use Microdata Sample. Estimates are weighted to be nationally representative. SSDI = Social Security Disability Insurance.

a

Arranged from most to least common based on weighted estimates for graduates with VI who worked full-time/full-year.

Several other factors had significant relationships with earnings of college graduates. Older age and having an advanced degree predicted higher earnings for all college graduates, but the effects of both variables were smaller for graduates with visual impairments. Being female and having one (or more) non-visual disability predicted lower earnings for all college graduates, but these effects were smaller for graduates with visual impairments. Class of worker was also associated with earnings for college graduates; compared to private for-profit workers, self-employed workers had slightly higher earnings, whereas non-profit and government workers had lower earnings. The earnings gaps between for-profit workers and non-profit and government workers were much smaller for people with visual impairments compared to the general population. Additional variables that predicted lower earnings for both groups included Black race, other race, Hispanic ethnicity, and SSDI receipt.

Discussion

The purpose of this study was to investigate factors associated with the earnings gap for college graduates with visual impairments. We primarily wanted to determine whether degree major explained some of the gap in earnings between people with visual impairments and the general population, so we first identified degree major categories for people with visual impairments and evaluated similarities and differences between the two groups. The most popular degree major categories for people with visual impairments were Business, Arts & Humanities, and Education. Comparing degree major categories, we found that only four major categories had greater than a 1 percentage point difference between the visually impaired and general populations. However, the degree categories with a higher percentage of people with visual impairments consisted of lower-paying fields (Education and Consumer Services & Recreation) while the categories with a higher percentage of the general population contained higher-paying fields (Engineering and Biological, Agricultural, & Environmental Sciences). These differences may explain some of the visual impairment earnings gap among college graduates.

Although our samples include working-age adults who may have obtained their college degree prior to experiencing vision loss, some individuals likely had their visual impairments from an early age. It is relevant to consider whether inaccessible education may have impacted the percentage of people with visual impairments who obtained degrees in high-earning technical fields. The many challenges to providing appropriate STEM education to students with visual impairments have been well-documented in the literature (Bell & Silverman, 2019; Cryer, 2013; Edwards & Green, 2014; Kahn & Lewis, 2014). This problem is so substantial at all levels of the educational system that researchers and other stakeholders have developed a national research agenda for STEM education for students with visual impairments (Lu & Wild, 2022). For students to succeed in a college Engineering program, one of the fields for which fewer graduates with visual impairments have a degree, they must have a strong foundation in mathematics, science, and technology. Therefore, their K-12 education must have adequately prepared them for the rigors of such a program, and this is not happening consistently for youth with visual impairments.

As documented in many previous studies (Bankrate, 2021; Carnevale et al., 2015; Cooper, 2021), we found that earnings differed by most degree majors for the entire sample. Only three majors were associated with a different amount of earnings for people with visual impairments: two earned substantially less on average (Accounting and Computer Science) while one earned substantially more (General Education). These findings support the conclusion that the relationship between earnings and specific degree major is generally not different for people with visual impairments than the general population, although there may be differences for a few majors.

Several other factors associated with earnings had different effects for people with visual impairments. The positive effects of age and having an advanced degree on earnings are attenuated for people with visual impairments: the positive effects are still present but are smaller. A smaller earnings premium for an advanced degree has also been documented for people with hearing impairments (Benito et al., 2016). The negative earnings effects of being female and of having a non-visual disability are also attenuated for people with visual impairments: the negative effects are not as large, although they are still present. Previous research has documented greater disadvantages in terms of employment for men with disabilities compared to women (Ballo, 2020; Sevak et al., 2015), and this research adds to that literature. The other factor that had a significantly different effect for people with visual impairments was class of worker: the negative earnings effect of working for private non-profit organizations and the government (compared to private for-profit organizations) was diminished for people with visual impairments. For example, the negative earnings differential for people with visual impairments who worked in a government job was about half that for members of the general population.

Even when controlling for demographic, class of worker, and education variables, average annual earnings for people with visual impairments were substantially lower than for the general population, regardless of degree major. Clearly, other factors will impact one’s earnings, including the specific job held. Although degree major can qualify people for certain jobs, the jobs people obtain may not relate to their major. In addition, having a visual impairment might impact the job a person is able to obtain, as some jobs require vision (e.g., pilot, surgeon) and therefore would not be open to someone who is blind or has a significant visual impairment. We know that women tend to obtain occupations with lower earnings potential relative to men, even with the same degree major (Sloane et al., 2021). Perhaps the same is true for people with visual impairments, compared to the general population. The Kessler Foundation study found that recent college graduates with disabilities were significantly less likely to work in regular, permanent positions and more likely to work in consultant or freelance jobs, or for temporary agencies (Phillips et al., 2022). These kinds of differences may exist for the people with visual impairments in this study. College graduates with disabilities are more likely to work in person-related, helping jobs than people without disabilities (Kessler Foundation, 2020), and many people with visual impairments work in the blindness and low vision field (Revillard, 2022), which may be by choice or related to lack of opportunities in other fields. Anecdotally, we know that some people with visual impairments work in a blindness-related field due to a lack of job opportunities in their degree field.

Limitations and Future Research Directions

This study has several limitations that should be acknowledged. First, the ACS data relies solely on self-report. Thus, earnings could be reported inaccurately, either intentionally or inadvertently. Also, we cannot confirm that people who reported visual impairment had an uncorrectable visual impairment or determine the severity of their impairment. We are also unable to establish when the person experienced vision loss, although given that visual impairment is associated with aging, many likely experienced vision loss after receiving their bachelor’s degree. In those cases, visual impairment would not have influenced their degree major, but it may have prompted them to change careers. Those factors and other variables that may be associated with earnings (e.g., the specific job held, job tenure) would be important to consider in future investigations of college graduates with visual impairments. Future research is also needed to evaluate differences in earnings of people with visual impairments and people without visual impairments for specific jobs.

Conclusion

College degree major is associated with earnings for all graduates, and the strength of the relationship differed for people with visual impairments and the general population for only three majors out of the 25 studied. Regardless of degree major and controlling for several important predictors of earnings, earnings for college graduates with visual impairments were significantly lower than earnings for the general population. Differences in earnings by specific degree major do not appear to be a key factor in this earnings disparity. However, differences in degree majors held by college graduates with visual impairments versus the general population may explain a small portion of the observed disparity in earnings, as people with visual impairments are more likely to hold degrees associated with lower earnings and less likely to hold high-earning degrees. It is vital that youth with visual impairments be informed about differences in earnings based on degree major, so they can make a fully informed choice regarding their college major. In addition, the K-12 educational system must adequately prepare youth with visual impairments in STEM to provide them with the foundational knowledge necessary to explore and pursue higher-earning majors, if that is their choice.

Acknowledgements

The authors have no acknowledgments.

Funding

The contents of this manuscript were developed under a grant from the U.S. Department of Health and Human Services, NIDILRR grant 90RTEM0007. However, these contents do not necessarily represent the policy of the Department of Health and Human Services and should not indicate endorsement by the Federal Government.

Footnotes

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethics Statement

This study did not require IRB approval as we utilized publicly available American Community Survey data.

Informed Consent

N/A

References

  1. Altonji JG, Kahn LB, & Speer JD. (2014). Trends in earnings differentials across college majors and the changing task composition of jobs. American Economic Review, 104(5), 387–393. 10.1257/AER.104.5.387 [DOI] [Google Scholar]
  2. Angle J, & Wissmann DA. (1981). Gender, college major, and earnings. Sociology of Education, 54(1), 25. 10.2307/2112510 [DOI] [Google Scholar]
  3. Arcidiacono P. (2004). Ability sorting and the returns to college major. Journal of Econometrics, 121(1–2), 343–375. 10.1016/j.jeconom.2003.10.010 [DOI] [Google Scholar]
  4. Ballo JG. (2020). Labour market participation for young people with disabilities: The impact of gender and higher education. Work, Employment and Society, 34(2), 336–355. 10.1177/0950017019868139 [DOI] [Google Scholar]
  5. Bankrate. (2021). Ranking the most and least valuable college majors. https://www.bankrate.com/pdfs/pr/20211004-college-majors-survey.pdf
  6. Bell EC, & Mino NM. (2013). Blind and visually impaired adult rehabilitation and employment survey: Final results. Journal of Blindness Innovation & Research, 3(1), 1–35. 10.5241/2f1-35 [DOI] [Google Scholar]
  7. Bell EC, & Silverman AM. (2019). Access to math and science content for youth who are blind or visually impaired. Journal of Blindness Innovation and Research, 9(1). 10.5241/9-152 [DOI] [Google Scholar]
  8. Benito SG, Glassman TS, & Hiedemann BG. (2016). Disability and labor market earnings: Hearing earnings gaps in the United States. Journal of Disability Policy Studies, 27(3), 178–188. 10.1177/1044207316658752 [DOI] [Google Scholar]
  9. Bureau of Labor Statistics. (2022). Usual weekly earnings of wage and salary workers: Vol. USDL-22–15. https://www.bls.gov/news.release/wkyeng.nr0.htm
  10. Capella ME. (2001). Predicting earnings of vocational rehabilitation clients with visual impairments. Journal of Rehabilitation, 67(4), 43–47. [Google Scholar]
  11. Carnevale AP, Cheah B, & Hanson AR. (2015). The economic value of college majors. https://cew.georgetown.edu/cew-reports/valueofcollegemajors/
  12. Cooper P. (2021). Is college worth it? A comprehensive return on investment analysis. https://freopp.org/is-college-worth-it-a-comprehensive-return-on-investment-analysis-1b2ad17f84c8
  13. Crudden A, McDonnall MC, & Tatch A. (2022). Unable to work? Characteristics of people with blindness and low vision who are out of the labor force. Disability and Health Journal, 101438. 10.1016/J.DHJO.2022.101438 [DOI] [PMC free article] [PubMed]
  14. Cruwys T, Haslam C, Steffens NK, Haslam SA, Fong P, & Lam BCP. (2019). Friendships that money can buy: Financial security protects health in retirement by enabling social connectedness. BMC Geriatrics, 19(1), 1–9. 10.1186/S12877-019-1281-1/FIGURES/2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cryer H. (2013). Teaching STEM subjects to blind and partially sighted students: Literature review and resources. RNIB Centre for Accessible Information. [Google Scholar]
  16. Dickler J. (2022). 41% of Americans say it’s “going to take a miracle” to be ready for retirement, report finds. CNBC. https://www.cnbc.com/2021/09/14/36percent-of-americans-say-they-wont-have-enough-to-retire-report-finds.html [Google Scholar]
  17. Eckstein Z, & Nagypál É. (2004). The evolution of U.S. earnings inequality: 1961–2002. Federal Reserve Back of Minneapolis Quarterly Review, 28(2), 10–29. 10.21034/qr.2822 [DOI] [Google Scholar]
  18. Edwards KM, & Green SL. (2014). Working with learners with visual impairments in STEM. In Green SL (Ed.), S.T.E.M. education: Strategies for teaching learners with special needs (pp. 143–155). Nova Science Publishers, Inc. [Google Scholar]
  19. Estrada-Hernández N. (2008). The effects of participant and service characteristics on the employment outcomes of RSA consumers with visual impairments: A follow-up on agency-type. Journal of Applied Rehabilitation Counseling, 39(1), 28–35. [Google Scholar]
  20. Horn L, & Berktold J. (1999). Students with disabilities in postsecondary education: A profile of preparation, participation, and outcomes. https://nces.ed.gov/pubs99/1999187.pdf
  21. Kahn S, & Lewis AR. (2014). Survey on teaching science to K-12 students with disabilities: Teacher preparedness and attitudes. Journal of Science Teacher Education, 25(8), 885–910. 10.1007/s10972-014-9406-z [DOI] [Google Scholar]
  22. Foundation Kessler. (2020). Report of main findings from the 2020 Kessler Foundation National Employment and Disability Survey: Recent college graduates. https://kesslerfoundation.org/sites/default/files/2020-05/2020-KesslerFoundation-Survey-Executive-Summary.pdf
  23. Kirkeboen LJ, Leuven E, & Mogstad M. (2016). Field of study, earnings, and self-selection. Quarterly Journal of Economics, 131(3), 1057–1111. 10.1093/QJE/QJW019 [DOI] [Google Scholar]
  24. Light A, & Schreiner S. (2019). College major, college coursework, and post-college wages. Economics of Education Review, 73, 101935. 10.1016/J.ECONEDUREV.2019.101935 [DOI] [Google Scholar]
  25. Lu X, & Wild T. (2022). Introducing the National Research Agenda in STEM Education for Students with Visual Impairments. Visual Impairment and Deafblind Education Quarterly, 67(3), 14–19. [Google Scholar]
  26. Lund EM, & Cmar JL. (2019a). A systematic review of factors related to employment outcomes in adults with visual impairments. Journal of Visual Impairment & Blindness, 113(6), 493–517. 10.1177/0145482X19885211 [DOI] [Google Scholar]
  27. Lund EM, & Cmar JL. (2019b). Factors related to employment outcomes in vocational rehabilitation consumers with visual impairments: A systematic review. Journal of Visual Impairment & Blindness, 113(6), 518–537. 10.1177/0145482X19885277 [DOI] [Google Scholar]
  28. McDonnall MC, Cmar JL, & McKnight Z. (2021). What predicts job quality of vocational rehabilitation consumers who are blind or have low vision? Rehabilitation Counseling Bulletin. 10.1177/00343552211060012 [DOI]
  29. McDonnall MC, Cmar JL, & McKnight ZS. (2022). Beyond employment rates: Earnings of people with visual impairments. Journal of Visual Impairment & Blindness, 116(4), 526–532. 10.1177/0145482X221121830 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. McDonnall MC, & Sui Z. (2019). Employment and unemployment rates of people who are blind or visually impaired: Estimates from multiple sources. Journal of Visual Impairment & Blindness, 113(6), 481–492. 10.1177/0145482X19887620 [DOI] [Google Scholar]
  31. McDonnall MC, & Tatch A. (2021). Educational attainment and employment for individuals with visual impairments. Journal of Visual Impairment and Blindness, 115(2), 152–159. 10.1177/0145482X211000963 [DOI] [Google Scholar]
  32. Modini M, Joyce S, Mykletun A, Christensen H, Bryant RA, Mitchell PB, & Harvey SB. (2016). The mental health benefits of employment: Results of a systematic meta-review. Australasian Psychiatry, 24(4), 331–336. 10.1177/1039856215618523 [DOI] [PubMed] [Google Scholar]
  33. Netemeyer RG, Warmath D, Fernandes D, & Lynch JG Jr. (2018). How am I doing? Perceived financial well-being, its potential antecedents, and its relation to overall well-being. Journal of Consumer Research, 45(1), 68–89. 10.1093/JCR/UCX109 [DOI] [Google Scholar]
  34. Paul S, Rafal MC, & Houtenville AJ. (2021). Annual disability statistics compendium: 2021. https://disabilitycompendium.org/
  35. Phillips KG, Nzamubona K, Neill JO, & Katz EE. (2022). Recent college graduates with disabilities: Higher education experiences and transition to employment. Journal of Postsecondary Education and Disability, 35(3), 213–228. [Google Scholar]
  36. PwC. (2016). Employee financial wellness survey: 2016 (Issue April). https://www.pwc.com/us/en/private-company-services/publications/assets/pwc-2016-employee-wellness-survey.pdf
  37. PwC. (2022). 2022 PwC employee financial wellness survey. https://www.pwc.com/us/en/services/consulting/business-transformation/library/assets/pwc-2022-employee-financial-wellness-survey-results.pdf
  38. Revillard A. (2022). Disabled people working in the disability sector: Occupational segregation or personal fulfilment? 10.1177/09500170221080401. 10.1177/09500170221080401 [DOI]
  39. Richardson JTE, & Roy AWN. (2002). The representation and attainment of students with a visual impairment in higher education. British Journal of Visual Impairment, 20(1), 37–48. 10.1177/026461960202000107 [DOI] [Google Scholar]
  40. Ryan C. (2012). Field of Degree and Earnings by Selected Employment Characteristics: 2011. In American Community Survey Briefs (ACSBR/11–10) (Issue October). U.S. Census Bureau. [Google Scholar]
  41. Sevak P, Houtenville AJ, Brucker DL, & O’Neill J. (2015). Individual characteristics and the disability employment gap. Journal of Disability Policy Studies, 26(2), 80–88. 10.1177/1044207315585823 [DOI] [Google Scholar]
  42. Sloane CM, Hurst EG, & Black DA. (2021). College majors, occupations, and the gender wage gap. Journal of Economic Perspectives, 35(4), 223–248. 10.1257/JEP.35.4.223 [DOI] [Google Scholar]
  43. Tamborini CR, Kim CH, & Sakamoto A. (2015). Education and lifetime earnings in the United States. Demography, 52(4), 1383–1407. 10.1007/s13524-015-0407-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. U.S. Census Bureau. (n.d.). American Community Survey and Puerto Rico Community Survey 2019 code list. U.S. Census Bureau. https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.2019.html [Google Scholar]
  45. U.S. Census Bureau. (2020). Understanding and using American Community Survey data: What all data users need to know. https://www.census.gov/programs-surveys/acs/guidance/handbooks/general.html
  46. U.S. Census Bureau. (2021a). American Community Survey 2015–2019 ACS 5-year PUMS Files: ReadMe.
  47. U.S. Census Bureau. (2021b). Class of worker. https://www.census.gov/topics/employment/industry-occupation/about/class-of-worker.html
  48. U.S. Census Bureau. (2021c). Understanding and using the American Community Survey public use microdata sample files: What data users need to know (Issue February). https://www.census.gov/programs-surveys/acs/microdata.html
  49. van der Noordt M, Ijzelenberg H, Droomers M, & Proper KI. (2014). Health effects of employment: A systematic review of prospective studies. Occupational and Environmental Medicine, 71(10), 730–736. [DOI] [PubMed] [Google Scholar]
  50. Webber DA. (2014). The lifetime earnings premia of different majors: Correcting for selection based on cognitive, noncognitive, and unobserved factors. Labour Economics, 28, 14–23. 10.1016/j.labeco.2014.03.009 [DOI] [Google Scholar]
  51. Webber DA. (2016). Are college costs worth it? How ability, major, and debt affect the returns to schooling. Economics of Education Review, 53, 296–310. 10.1016/j.econedurev.2016.04.007 [DOI] [Google Scholar]

RESOURCES