08832323.2014.988204
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Overeducation and Employment Mismatch: Wage
Penalties for College Degrees in Business
Ihsuan Li, Mathew Malvin & Robert D. Simonson
To cite this article: Ihsuan Li, Mathew Malvin & Robert D. Simonson (2015) Overeducation and
Employment Mismatch: Wage Penalties for College Degrees in Business, Journal of Education
for Business, 90:3, 119-125, DOI: 10.1080/08832323.2014.988204
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Date: 11 January 2016, At: 19:12
JOURNAL OF EDUCATION FOR BUSINESS, 90: 119–125, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2014.988204
Overeducation and Employment Mismatch: Wage
Penalties for College Degrees in Business
Ihsuan Li, Mathew Malvin, and Robert D. Simonson
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
Minnesota State University, Mankato, Mankato, Minnesota, USA
Overeducation and underemployment are of increasing national concern. Recent research
estimates that 48% of workers are overeducated for their positions. The wage penalty for
overeducation varies significantly across majors by gender. Using the American Community
Survey (Ruggles et al., 2010), the authors examine the extent of overeducation among businessrelated majors. This article contributes to the literature with detailed results of the wage
penalties by gender for each of the 13 business-related majors, controlling for occupational and
industry classifications. Overall, this authors find the penalty for overeducation among most
business-related majors to vary from 4% to 14%. Overeducated women in business-related
majors, however, appear to suffer lower wage penalties compared to other majors.
Keywords: business majors, human capital, overeducation, returns to college education
Investment in human capital is widely accepted as generating positive returns to individuals as well as the nation.
Generally, human capital accumulates through education
leading to increased productivity and wages. Education
also generates positive spillover effects leading to higher
economic growth and improved standard of living
(Weisbrod, 1964).
In the United States, college degree holders presently
account for approximately 30.4% of the population representing an almost fourfold increase since the 1960s (U.S.
Census Bureau, 2013). Among the factors driving the
increase in college enrollment is the widening gap between
lifetime expected earnings between college graduates and
those with a high school degree only, known as the college
wage premium (Katz & Murphy, 1992). According to the
National Center for Educational Statistics (2014), the
median earnings for bachelor degree holders between 25
and 34 years old in 2010 was approximately $45,000, compared to $21,000 for high school graduates. The tremendous growth in college enrollment also resulted in
increasing student loan debt.
In a major study on salaries of college graduates,
Carnevale, Strohl, and Melton (2010) calculated the earnings of college graduates at the 25th and 75th percentile for
each of 171 college majors. The survey showed some
Correspondence should be addressed to Ihsuan Li, Minnesota State
University, Mankato, Department of Economics, 150 Morris Hall,
Mankato, MN 56001, USA. E-mail: Ihsuan.li@mnsu.edu
business majors (business management, business, and
accounting) earned a median income ranging from $40,000
to $95,000. As a group, business students had the third
highest median income ($60,000), after computer science
and mathematics ($70,000), and engineering ($75,000).
However, with a current underemployment nearing 50%
among college graduates (Vedder, Denhart, & Robe, 2013)
the American public has also become more concerned about
the pecuniary burden of student loan debt versus the wage
premium associated with a college degree. Here we seek to
provide an insight into overeducation among businessrelated majors and by gender. Business-related majors represent the top three most popular majors among all college
graduates in the United States, and female college graduates
make up approximately half of that population (Carnevale
et al., 2010).
REVIEW OF THE LITERATURE
Overeducation is not a recent phenomenon. Hecker (1992)
and Vedder et al. (2013) have documented evidence of
overeducation since the 1970s, increasing approximately
from 11% in 1970 to 48% in 2010. Substantial research has
examined this trend, with improved measurement techniques and varied hypotheses.
Research to date has established four general findings:
(a) workers who have never married have lower incidences
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
120
I. LI ET AL.
of overeducation (Robst, 2008), (b) the probability of being
overeducated is higher for women (Battu, Belfield, &
Sloane, 2000; Buchel & Battu, 2003), and the penalty is
lower (Daly, Buchel, & Duncan, 2000), (c) the effects of
overeducation do not diminish with age (Battu et al.,
2000), and (d) overeducation varies by profession and occupation. For example, managers, leaders, entrepreneurs, and
workers in positions requiring communication, planning,
and literacy were more likely to be overeducated (Chevalier
& Lindley, 2009; Green & McIntosh, 2007). The overeducation penalty for individuals with the same educational
attainment and in jobs with similar schooling requirements
has ranged between 13% (Verdugo & Verdugo, 1989) to
20% or more (Chevalier, 2003; Robst, 2008). Similarly, the
overeducation penalty varies by gender, but the difference
is not significant (21.6% for men and 19.1% for women;
Robst, 2008).
Explanations for the causes of overeducation fall mainly
into three groups: (a) individuals become overeducated to
compensate for fewer advancement opportunities (the
advancement hypothesis; Buchel & Mertens, 2004), (b)
worker heterogeneity among college graduates (the heterogeneity hypothesis; Chevalier, 2003), and (c) oversupply of
low demand majors (Vedder et al., 2013). The heterogeneity hypothesis argues that as access to higher education
widens, lower ability students join higher ability students in
the college graduate pool (Chevalier, 2003). However,
research controlling for matched counterparts, including
gender and ability levels cannot explain the persistence of
overeducation in the data (McGuinness, 2003; McGuinness,
2006; Green, McIntosh, & Vignoles, 1999). Although some
evidence supports the advancement hypothesis, more recent
papers find evidence to support the signaling hypothesis.
The signaling hypothesis argues that a college degree has
lost its value as a signal device to prospective employers as
more students graduate with college degrees (Vedder et al.,
2013).
Finally, research findings are limited by the difficult of
properly measuring overeducation. Subjective and objective measures have been used to evaluate whether an individual is overeducated. Subjective measures estimated the
incidence from 17% to 32% (Chevalier, 2003). Objective
measures, such as the one used in this paper, found a
slightly wider incidence range 17% to 42% (Battu et al.,
2000, Daly et al., 2000; Duncan & Hoffman, 1981).
This article contributes to the literature on overeducation
by controlling for industry classifications and occupational
codes, and provides overeducation penalties estimates for
13 business-related majors by gender. The addition of
industry and occupational classifications allows us disentangle the overeducation estimates by business and industry
specific skills. It follows the preferred convention of measuring overeducation as more than one standard deviation
above modal educational attainment within the occupation.
Finally, we use the 2011 (annual) American Community
Survey (ACS; Ruggles et al., 2010) to better capture the
effect of the growth in college enrollment experienced in
the last decade.
MODEL
The dominant framework for explaining earnings distributions within developed economies was established by Gary
Becker (1964). The Mincerian earnings model (1974) later
provided the empirical structure to assess and measure
human capital. National interest in human capital accumulation is based on the economic theory that it leads to
increased productivity, income, and economic growth.
A direct measurement of human capital is not possible.
The established convention follows the Mincerian earnings
equation, which explains earnings as a function of human
capital accumulated through education and work experience:
Earnings D f ðEducation; ExperienceÞ:
Educational attainment is measured by years of education completed and by highest degree attained (high school,
college, graduate, and professional). This model assumes
college education adds to human capital by enhancing time
management, accountability, and critical thinking skills,
which in theory make an individual more productive in the
workplace. Yet, some college majors are more narrowly tailored in skill sets (such as Accounting compared to general
business or business management). Therefore, we control
for the different majors within business degrees.
Here we employ the objective measure of overeducation
(Battu et al., 2000). To derive the measure, we calculate
the required education level by position, and estimate overeducation with respect to the modal required educational
level for a given position:
ReqEdu (required education): modal educational attainment
plus or minus 1 standard deviation
Edu (matched-education): Edu D ReqEdu
OverEdu (overeducation): OverEdu D Edu–ReqEdu if
Edu > ReqEdu
UnderEdu (undereducation): UnderEdu D ReqEdu–Edu if
ReqEdu > Edu.
Following the model specified by Verdugo and Verdugo
(1998), the theoretical expression is as follows:
Wage Income D f Education; Experience; Major; Xij ;
Where Xij is a vector consisting of observable individual characteristics, such as race (Robst, 2001), marriage status, presence of children (Green et al., 2002),
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
OVEREDUCATION PENALTIES IN BUSINESS MAJORS
and gender (female) to capture the significant differences by gender for overeducation, especially in the presence of children under 18 years old (Green et al., 2002;
McGoldrick & Robst, 1996).
Interaction terms are added in the empirical equation to
isolate the effect of mismatched education to employment in
each of the business majors (Robst, 2001). We also construct
a proxy for years of work experience by subtracting the total
years of schooling from the individuals’ age. This proxy
introduces measurement error in the empirical model, but as
the proxy would overestimate the experience variable in the
likely presence of worker heterogeneity, its effect should
cancel out (Verhaest & Omey, 2012). In addition, veteran,
disability, federal government employee, and citizenship are
included to capture important characteristics, which affect a
worker’s wage rate. For control variables, we add state dummies to capture state fixed effects, as well as industry (16),
occupation (25), and metro area dummies.
The final expression for the model is the following:
Wage Income D f (Education, Experience, Business
Degree Majors, Female, Has Young Children, Marriage
Status, Race, Veteran, Disability, Federal Government
Employee, Citizenship, Metro, Occupation and Industry
Classifications).
DATA
The data were obtained from the 2011 annual ACS (Ruggles et al., 2010). The ACS is an annual and multiyear survey conducted by the U.S. Census Bureau. The sample
used in this study included individuals who had worked at
least 40 hr a week in the previous year and earned nonzero
wage income. We limited our analysis to individuals with a
bachelor degree and holding one of the 13 business-related
majors (see Table 1).
The summary statistics is based on weighted data. The
sample consists of 20,659,080 observations. The average
individual in this sample is 43 years old, has 21 years of
experience; 28.5% of surveyed individuals has kids younger than 13 years old; 60.7% are married; 77.3% are White,
6.6% are Hispanic, 6.6% are Black; 45% are women; and
81% live in a metropolitan area. Among all individuals surveyed, 20.25% have a bachelor’s degree (highest attained
educational level). In this study, we define an individual to
be overeducated for his or her position if workers in the
same position are more than one standard deviation from
the mode. According to this definition, among all individuals who have attained a bachelor’s degree, 5.7% are said to
be overeducated for their position. Among the control variables, the dataset includes all 50 states, 16 industries, and
25 occupational codes. We coded variable has young children to equal one if the individual has children younger
than 13 years old. Among Asian individuals (race) we
121
TABLE 1
Sociodemographic Summary Statistics (Weighted Averages)
Variable
Wage income (U$)
Age (years)
Experience (years)
Has small children
Married
Divorced
White
Black
Asians
Hispanics
Native Americans
Other Asians
Other Races
Foreign born
Female
Male
Metro area
Veteran
Disability
Federal government employee
Average
Minimum
Maximum
$70,767.42
41.92
19.95
29.41%
62.86%
9.51%
74.98%
8.01%
3.80%
7.35%
0.34%
4.08%
1.45%
13.44%
44.86%
55.14%
84.82%
6.41%
2.94%
4.89%
$4
19
0
$607,000
94
72
included Chinese, Indian, and Korean individuals. Other
Asian individuals includes all other Asian ethnic groups.
We also control for the presence of federal government
employees, those with disabilities, veterans, and living in
metropolitan areas.
We analyze wage differential data for 13 business-related
majors by gender; to our knowledge, this is the most detailed
research on overeducation of this type. We coded the following majors: economics, general business, accounting,
actuarial science, business and management administration,
operations logistic and e-commerce, business economics,
marketing and marketing research, finance, human resources
and personnel management, international business, hospitality management, management information system, and miscellaneous business administration (see Table 2).
The modal wage income by majors range $30,000 (hospitality) to $100,000 (finance and economics). In the sample, 4.7% of individuals with a bachelor degree are
overeducated for their position. The incidence of overeducation among business-related majors is less than doubled
(7.2%). Women in business-related majors make up about
half (3%), and they major mostly in business management,
general business, marketing, and accounting. The top four
majors among all overeducated individuals in this sample
were business management, general business, accounting,
and marketing. The major with the least overeducated
major was actuarial science.
METHODOLOGY
Since we are interested in the penalty from overeducation
on wage income growth, we estimated the following
122
I. LI ET AL.
TABLE 2
Summary Statistics of Business-Related Majors by Gender
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
Business-related
majors
Economics
General business
Accounting
Actuarial science
Business management
Operations and logistics
Business economics
Marketing
Finance
Human resources
International business
Hospitality
Management information system
Miscellaneous business
Total
Number of
overeducated workers:
Both genders
Overeducated males
Overeducated females
Proportion of
overeducated
women to men
90,086
329,407
176,819
649
523,654
11,565
8,164
155,914
97,939
28,869
14,230
35,612
20,558
19,087
1,512,552
61,688
206,901
89,902
225
304,333
8,439
6,710
78,424
65,144
12,815
7,578
16,839
13,639
10,505
883,143
28,398
122,506
86,917
424
219,321
3,126
1,454
77,490
32,794
16,054
6,652
18,773
6,919
8,582
629410
0.46
0.59
0.97
1.88
0.72
0.37
0.22
0.99
0.50
1.25
0.88
1.11
0.51
0.82
0.71
Wage income
(mode)
100000
40000
50000
60000
50000
60000
60000
50000
100000
50000
50000
30000
80000
40000
Note: The sample (weighted) includes only individuals who worked full time (usual work hours of 40 or more per week), earned nonzero wage income,
and whose highest educational attainment includes only a bachelor’s degree.
empirical equation:
LnðWageIncomeÞ D B0 C B1 Age C B2 Experience
C B3 Has Young Kids C B4 Divorced C B5 Blacks
C B6 Asians C B7 Hispanics C B8 Native Americans
C B9 OtherAsians C B10 Other Races C B11 Foreign Born
C B12 Female C B13 Metro Area C Bð14 ¡ 27Þ Major OverEdu
C Bð28 » 41Þ Major OverEdu Female C B42 Veteran
C B43 Disability C B44 Federal Government Employee
C Bð45 ¡ 94Þ State Dummies C Bð95 ¡ 110Þ Industry Dummies
C Bð111 ¡ 135Þ Occupation Dummies:
We estimated two sets of regressions: (a) the benchmark linear model (with and without the full set of controls) using ordinary least squares (OLS) with robust
standard errors; and (b) a two-stage least-squared
method (2SLS; with and without the full set of controls). The predicted variable is log of wage income.
The predictor variables include standard socioeconomic
measures (SEM), indicator variables for individuals who
are overeducated for their position by their college
major, interaction term of the female variable with each
of the indicator variables for overeducated individuals
by major, and other control variables (states, industries,
and occupations). Omitted in the equation, and therefore
serving as benchmarks, are variables White (race) and
male (gender), non–foreign born, Hawaii (state), mining
(industry), and active military (occupation). We used
Stata (ver. 13, StataCorp LP, College Station, TX) to
run the regressions.
RESULTS AND DISCUSSIONS
We discuss the results of the 2SLS regressions because
OLS estimates are biased in the presence of endogeneity.
The set of results from the 2SLS regression controls for
state of residence, industries, occupations, and other socioeconomic background. The overall fit of the model is .29
(R2). We estimated the coefficients with robust standard
errors. The results of the full regressions are available upon
request. Among the overeducated business-related majors,
the penalties range from 4.4% (general business) to 13.8%
(accounting). Two majors enjoyed a premium for overeducation (actuarial sciences and operations logistics). The
estimated coefficients were significantly different from
zero for economics (–12%), general business (–4.4%), business management (–7.7%), marketing (–5.7%), hospitality
(¡8.4%), and human resources (–11%). These values fall
within the range established by previous research (see
Table 3).
Ceteris paribus, females experience a 17.17% lower wage
relative to men. However, among the overeducated in business-related majors (excluding actuarial science, international business, and general business), women in
accounting, business management, logistics, and business
economics realized a smaller wage penalty. Once we collect
the coefficients for (a) overeducation by major and gender,
(b) women, (c) overeducation by majors, the penalty from
overeducation is reduced for some, and increased for others.
Despite the overeducation premium for all actuarial science
majors, once gender is considered, an overeducated female
actuarial science major enjoys only a 2% premium over all
other majors and male individuals. Conversely, women in
OVEREDUCATION PENALTIES IN BUSINESS MAJORS
123
TABLE 3
Summary Regression Results
OLS
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
Explanatory variables
Age
Experience
Has small children
Divorced
Black
Asians
Hispanics
Native American
Other Asians
Other races
Foreign born
Female
Metro
2SLS
Coefficient Robust SE Coefficient Robust SE Coefficient Robust SE Coefficient Robust SE
0.0155 **
0.2301 **
¡0.0351 **
¡0.1868 **
0.0577 **
¡0.1618 **
¡0.1384 **
¡0.0989 **
¡0.0925 **
¡0.0900 **
¡0.2709 **
0.2739 **
0.0001
0.0032
0.0052
0.0055
0.0090
0.0061
0.0203
0.0089
0.0124
0.0060
0.0031
0.0036
0.0142 **
0.2053 **
¡0.0295 **
¡0.1424 **
¡0.0521 **
¡0.1376 **
¡0.0614 **
¡0.1355 **
¡0.0976 **
¡0.1000 **
¡0.1804 **
0.1563 **
0.0001
0.0030
0.0048
0.0053
0.0084
0.0058
0.0188
0.0084
0.0114
0.0055
0.0031
0.0038
¡0.0156 **
0.0113 **
0.1309 **
0.0034
¡0.2125 **
0.0171 *
¡0.2247 **
¡0.1648 **
¡0.1437 **
¡0.1500 **
¡0.0498 **
¡0.2607 **
0.2648 **
0.0001
0.0008
0.0076
0.0058
0.0058
0.0094
0.0073
0.0203
0.0094
0.0130
0.0065
0.0030
0.0037
¡0.0143 **
0.0100 **
0.1179 **
0.0044
¡0.1652 **
¡0.0883 **
¡0.1931 **
¡0.0847 **
¡0.1752 **
¡0.1486 **
¡0.0643 **
¡0.1716 **
0.1484 **
0.0001
0.0007
0.0072
0.0054
0.0055
0.0088
0.0070
0.0189
0.0089
0.0120
0.0060
0.0031
0.0038
¡0.3313 **
¡0.2622 **
¡0.3725 **
0.6849 **
¡0.2827 **
¡0.1678 **
¡0.2770 **
¡0.2768 **
¡0.2192 **
¡0.3409 **
¡0.2316 **
¡0.4487 **
¡0.1541 **
¡0.2545 **
0.0276
0.0160
0.0232
0.2199
0.0113
0.0538
0.0845
0.0249
0.0280
0.0495
0.0796
0.0499
0.0529
0.0526
¡0.1204 **
¡0.0436 **
¡0.1382 **
0.8253 **
¡0.0776 **
0.0396
¡0.0822
¡0.0561 *
¡0.0288
¡0.1092 *
0.0093
¡0.0783
0.0109
¡0.0194
0.0269
0.0156
0.0223
0.2600
0.0112
0.0491
0.0778
0.0244
0.0260
0.0478
0.0761
0.0503
0.0541
0.0532
¡0.3307 **
¡0.2623 **
¡0.3722 **
0.6847 **
¡0.2821 **
¡0.1670 **
¡0.2764 **
¡0.2774 **
¡0.2169 **
¡0.3402 **
¡0.2300 **
¡0.4575 **
¡0.1530 **
¡0.2531 **
0.0277
0.0160
0.0232
0.2201
0.0113
0.0538
0.0845
0.0249
0.0280
0.0495
0.0797
0.0499
0.0529
0.0526
¡0.1196 **
¡0.0435 **
¡0.1376 **
0.8254 **
¡0.0769 **
0.0405
¡0.0813
¡0.0565 **
¡0.0265
¡0.1084 **
0.0108
¡0.0840 y
0.0120
¡0.0181
0.0269
0.0156
0.0223
0.2599
0.0112
0.0492
0.0778
0.0243
0.0260
0.0478
0.0761
0.0500
0.0541
0.0532
0.1224 *
0.0274
0.1022 **
¡0.7349 *
0.0688 **
0.3392 **
0.4424 **
0.1539 **
0.0244
0.0922
0.0724
0.1863 **
0.1127
0.0777
¡0.0941 **
¡0.2057 **
0.1588 **
10.4908 **
No
No
No
200,817
0
0.1619
0.0490
0.0249
0.0319
0.3121
0.0161
0.0964
0.1313
0.0326
0.0428
0.0674
0.1089
0.0660
0.0836
0.0771
0.0065
0.0091
0.0055
0.0048
0.0723
¡0.0062
0.0698 *
¡0.6763 *
0.0453 **
0.1805 *
0.3062 *
0.1110 **
¡0.0080
0.0675
¡0.0129
0.1264 *
0.0835
0.0264
¡0.0829 **
¡0.1633 **
0.2005 **
11.1441 **
Yes
Yes
Yes
200,817
0
0.2892
0.0472
0.0238
0.0307
0.3287
0.0156
0.0900
0.1224
0.0317
0.0402
0.0653
0.1027
0.0679
0.0824
0.0796
0.0061
0.0084
0.0063
0.0253
0.1225 **
0.0273 **
0.1027
¡0.7340 **
0.0680 **
0.3394 **
0.4437 **
0.1539 **
0.0220
0.0925
0.0725
0.1914 **
0.1123
0.079
0.0490
0.0249
0.0319
0.3127
0.0161
0.0964
0.1313
0.0326
0.0427
0.0674
0.1089
0.0661
0.0836
0.0771
0.0723
¡0.0068 **
0.0699 **
¡0.6759 **
0.0441 **
0.1805 **
0.3074 **
0.1105
¡0.0100
0.0676
¡0.0127 y
0.1310
0.0833
0.0257
0.0472
0.0307
0.3287
0.0157
0.0901
0.1224
0.0316
0.0401
0.0653
0.1028
0.0676
0.0824
0.0796
0.0238
¡0.1446 **
0.1339 **
10.4260 **
No
No
No
201,074
0
0.1631
0.0102
0.0055
0.0205
¡0.1094 **
0.1783 **
10.5194 **
Yes
Yes
Yes
201,074
0
0.2902
0.0094
0.0063
0.0322
Overeducated by major
Economics OE
General business OE
Accounting OE
Actuarial science OE
Business management and administration OE
Operations logistics and e-commerce OE
Business economics OE
Marketing OE
Finance OE
HR and personnel management OE
International business OE
Hospitality management OE
Management information system OE
Miscellaneous business administration OE
Overeducated by major interacted with female
Economics OE*Female
General business OE*Female
Accounting OE*Female
Actuarial science OE*Female
Business management and administration OE*Female
Operations logistics and e-commerce OE*Female
Business economics OE*Female
Marketing OE*Female
Finance OE*Female
HR and personnel management OE*Female
International business OE*Female
Hospitality management OE*Female
Management information system OE*Female
Miscellaneous business administration OE*Female
Veteran
Disability
Federal government employee
Constant
Industry fixed effect
Occupation fixed effect
State fixed effect
Observations
Prob>F
R2
Note: The sample data include only individuals who were sampled in the annual ACS 2011 survey, worked full time, earned nonzero income, and had only
a bachelor’s degree. The regressions are unweighted.
y
p .10. *p .05. **p .01.
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124
I. LI ET AL.
accounting, economics, general business, business management, finance, and human resources all suffer a net wage
penalty of approximately 20% relative to all men and
majors. There are two exceptions that are significant and
positive: females who are overeducated for their positions
and majored in operations and logistics and business economics enjoy a net premium of 5% and 5.4%, respectively,
over all men and other majors (see Table 4).
Other variables that are significant in the results are disability (penalty of 11%), and federal government employee
(premium of 17.8%).
Once industry and occupation classifications are controlled (albeit a rough approximation), the wage penalties
for overeducated business majors are significantly reduced
(compared to previous literature and regression results without controls). In general, the estimates of overeducation are
within the range found in the literature but narrower. However, the extent of the impact is not homogenous across business majors, especially for women. One potential
explanation for the unequal impact by major is the inability
of college graduates to clearly signal potential productivity
due to pooling in the labor market. Another possible explanation is the specialized nature of some business majors,
such as actuarial science and accounting, which require
licensure to practice the profession as actuary and certified
public accountant. Licensing exams may serve as a screening mechanism, which eliminates weaker candidates from
seeking the degree, therefore thinning the lower end of the
abilities distribution tail. Interestingly, overeducated women
who majored in business economics and operations logistics
do not suffer any wage penalties but rather enjoy a premium
for overeducation compared to men and all other majors
(which suggests need for further investigation). Finally,
females in most business majors suffer a lower wage penalty
TABLE 4
Summary of Wage Penalties for Overeducated Business Related
Majors by Gender
Business related majors
Economics
General business
Accounting
Actuarial science
Business management
Operations and logistics
Business economics
Marketing
Finance
Human resources
International business
Hospitality
Management information system
Miscellaneous business
Men
Women
¡11.96*
¡4.35
¡13.76*
82.54
¡7.69
4.05
¡8.13
¡5.65
¡2.65
¡10.84*
1.08
¡8.40
1.20
¡1.81
¡21.89
¡22.18*
¡23.93*
¡2.21*
¡20.45*
4.95*
5.44*
¡11.76
¡20.81
¡21.24
¡17.36*
¡12.46
¡7.63
¡16.40
Note: Wage differential (in percent) relative to all other majors. *Significant at 10% level.
for being overeducated compared to overeducated males in
the same business-related majors.
CONCLUSION AND IMPLICATIONS
Overeducation and the associated wage penalty have been
studied extensively. The literature finds that the individuals
who are overeducated in fields that were not related to their
studies were subjected to three times the penalty of those
whose degree majors were related (Chevalier, 2003). However, among business-related majors, overeducation carries
dissimilar wage penalties. Further, when gender is taken into
account, female workers who are overeducated in a business-related major suffer a lower wage penalty relative to
females in other majors. Two majors in this paper are of particular interest: women overeducated in operations logistics
and business economics majors enjoyed a net premium of
about 5%.
This paper examined ACS data for business-related
majors to answer the question of whether overeducation
wage penalty is still present and whether gender has an additional impact on the penalty. The results for some of the 13
overeducated business-related majors were not significantly
different from zero. For most majors, the penalty was narrower than found in previous literature. There is heterogeneity across business-related majors, with actuarial science,
business economics, and operation logistics showing unusual
values. The implication of the results suggest that while overeducation in general penalizes the worker for a given position, females in business and related majors are not penalized
as heavily compared to females in other majors who are overeducated for their positions. Future researchers should consider additional control variables for detailed occupational
ranks as well as a more precise measure of years for work
experience for more robust results.
REFERENCES
Battu, H., Belfield, C., & Sloane, P. (2000). How well can we measure
graduate over education and its effects? National Institute Economic
Review, 171, 82–93.
Battu, H., & Sloane, P. J. (2002). To what extent are ethnic minorities in
Britain overeducated. International Journal of Manpower, 23, 192–207.
Becker, G. (1964). Human capital: A theoretical and empirical analysis
with special reference to education. New York, NY: Columbia University Press.
Buchel, F., & Battu, H. (2003). The theory of differential over-qualification: Does it work? Scottish Journal of Political Economy, 50, 1–16.
Buchel, F., & Mertens, A. (2004). Over-education, undereducation, and the
theory of career mobility. Applied Economics, 36, 803–816.
Carnavale, A. P., Strohl, J., & Melton, M. (2011). What’s it worth? The
economic value of college majors. Washington, DC: Georgetown University Center on Education and the Workforce.
Chevalier, A. (2003). Measuring over-education. Economica, 70, 509–531.
Chevalier, A., & Lindley, J. (2009). Over-education and the skills of UK
graduates. Journal of the Royal Statistical Society, 172, 307–337.
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
OVEREDUCATION PENALTIES IN BUSINESS MAJORS
Daly, M. C., Buchel, F., & Duncan, G. J. (2000). Premiums and penalties
for surplus and deficit education: Evidence from the United States and
Germany. Economics of Education Review, 19, 169–178.
Duncan, J., & Hoffman, S. (1981). The incidence and wage effects of overeducation. Economics of Education Review, 1, 75–86.
Green, F., & McIntosh, S. (2007). Is there genuine underutilization of skills
amongst the over-qualified. Applied Economics, 39, 427–439.
Green, F., McIntosh, S., & Vignoles, A. (1999). Over-education and skills:
Clarifying the concepts. London, England: Center for Economic
Performance.
Green, F., McIntosh, S., & Vignoles, A. (2002). The utilization of education and skills: Evidence from Britain. The Manchester School, 70(60),
792–811.
Hecker, D. E. (1992). Reconciling conflicting data on jobs for college graduates. Monthly Labor Review, 115, 3–12.
Katz, L., & Murphy, K. M. (1992). Changes in relative wages, 1963–1987: Supply and demand factors. The Quarterly Journal of Economics, 107, 35–78.
McGoldrick, K., & Robst, J. (1996). Gender differences in over-education:
A test of the theory of differential over-qualification. The American Economic Review, 86(2), 280–284.
McGuinness, S. (2003). Graduate over-education as a Sheepskin effect:
Evidence from Northern Ireland. Applied Economics, 35, 597–608.
McGuinness, S. (2006). Over-education in the labour market. Journal of
Economic Surveys, 20, 387–418.
National Center for Education Statistics. (2014). The condition of education 2014 (NCES 2014-083), annual earnings of young adults. Washington, DC: National Center for Education Statistics, U.S. Department of
Education.
125
Robst, J. (2001). Post school investment and wage differntials: Some further evidence. Southern Economic Journal, 121–132.
Robst, J. (2008). Over-education and college major: Expanding the definition of mismatch between schooling and jobs. The Manchester School,
76, 349–368.
Ruggles, S., Alexander, J. T., Genadek, K., Goeken, R., Schroeder, M. B.,
& Sobek, M. (2011). Integrated public use microdata series: Version
5.0 [Machine readable database]. Minneapolis, MN: Minnesota Population Center.
Sloane, P., Battu, H., & Seaman, P. (1996). Over-education and the formal
education/experience and training trade-off. Applied Economics Letters,
3, 511–515.
Sloane, P., Battu, H., & Seaman, P. (1999). Over-education, undereducation and the Brittish labour market. Applied Economics, 31,
1437–1453.
Verdugo, R. R., & Turner Verdugo, N. (1988). The impact of surplus
schooling on earnings. The Journal of Human Resources, 24,
629–643.
Verhaest, D., & Omey, E. (2012). Over-education, undereducation and
earnings: Further evidence on the importance of ability and measurement bias. Journal of Labor Resources, 33, 76–90.
Vedder, R., Denhart, C., & Robe, J. (2013, January 24). Why are recent
college graduates underemployed? University enrollments and labor
market realities. Center for College Affordability and Productivity.
Retrieved from http://centerforcollegeaffordability.org/uploads/
Underemployed%20Report%202.pdf
Weisbrod, B. A. (1964). External benefits of public education: An economic analysis. Princeton, NJ: Princeton University.
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Overeducation and Employment Mismatch: Wage
Penalties for College Degrees in Business
Ihsuan Li, Mathew Malvin & Robert D. Simonson
To cite this article: Ihsuan Li, Mathew Malvin & Robert D. Simonson (2015) Overeducation and
Employment Mismatch: Wage Penalties for College Degrees in Business, Journal of Education
for Business, 90:3, 119-125, DOI: 10.1080/08832323.2014.988204
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Date: 11 January 2016, At: 19:12
JOURNAL OF EDUCATION FOR BUSINESS, 90: 119–125, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2014.988204
Overeducation and Employment Mismatch: Wage
Penalties for College Degrees in Business
Ihsuan Li, Mathew Malvin, and Robert D. Simonson
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Minnesota State University, Mankato, Mankato, Minnesota, USA
Overeducation and underemployment are of increasing national concern. Recent research
estimates that 48% of workers are overeducated for their positions. The wage penalty for
overeducation varies significantly across majors by gender. Using the American Community
Survey (Ruggles et al., 2010), the authors examine the extent of overeducation among businessrelated majors. This article contributes to the literature with detailed results of the wage
penalties by gender for each of the 13 business-related majors, controlling for occupational and
industry classifications. Overall, this authors find the penalty for overeducation among most
business-related majors to vary from 4% to 14%. Overeducated women in business-related
majors, however, appear to suffer lower wage penalties compared to other majors.
Keywords: business majors, human capital, overeducation, returns to college education
Investment in human capital is widely accepted as generating positive returns to individuals as well as the nation.
Generally, human capital accumulates through education
leading to increased productivity and wages. Education
also generates positive spillover effects leading to higher
economic growth and improved standard of living
(Weisbrod, 1964).
In the United States, college degree holders presently
account for approximately 30.4% of the population representing an almost fourfold increase since the 1960s (U.S.
Census Bureau, 2013). Among the factors driving the
increase in college enrollment is the widening gap between
lifetime expected earnings between college graduates and
those with a high school degree only, known as the college
wage premium (Katz & Murphy, 1992). According to the
National Center for Educational Statistics (2014), the
median earnings for bachelor degree holders between 25
and 34 years old in 2010 was approximately $45,000, compared to $21,000 for high school graduates. The tremendous growth in college enrollment also resulted in
increasing student loan debt.
In a major study on salaries of college graduates,
Carnevale, Strohl, and Melton (2010) calculated the earnings of college graduates at the 25th and 75th percentile for
each of 171 college majors. The survey showed some
Correspondence should be addressed to Ihsuan Li, Minnesota State
University, Mankato, Department of Economics, 150 Morris Hall,
Mankato, MN 56001, USA. E-mail: Ihsuan.li@mnsu.edu
business majors (business management, business, and
accounting) earned a median income ranging from $40,000
to $95,000. As a group, business students had the third
highest median income ($60,000), after computer science
and mathematics ($70,000), and engineering ($75,000).
However, with a current underemployment nearing 50%
among college graduates (Vedder, Denhart, & Robe, 2013)
the American public has also become more concerned about
the pecuniary burden of student loan debt versus the wage
premium associated with a college degree. Here we seek to
provide an insight into overeducation among businessrelated majors and by gender. Business-related majors represent the top three most popular majors among all college
graduates in the United States, and female college graduates
make up approximately half of that population (Carnevale
et al., 2010).
REVIEW OF THE LITERATURE
Overeducation is not a recent phenomenon. Hecker (1992)
and Vedder et al. (2013) have documented evidence of
overeducation since the 1970s, increasing approximately
from 11% in 1970 to 48% in 2010. Substantial research has
examined this trend, with improved measurement techniques and varied hypotheses.
Research to date has established four general findings:
(a) workers who have never married have lower incidences
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120
I. LI ET AL.
of overeducation (Robst, 2008), (b) the probability of being
overeducated is higher for women (Battu, Belfield, &
Sloane, 2000; Buchel & Battu, 2003), and the penalty is
lower (Daly, Buchel, & Duncan, 2000), (c) the effects of
overeducation do not diminish with age (Battu et al.,
2000), and (d) overeducation varies by profession and occupation. For example, managers, leaders, entrepreneurs, and
workers in positions requiring communication, planning,
and literacy were more likely to be overeducated (Chevalier
& Lindley, 2009; Green & McIntosh, 2007). The overeducation penalty for individuals with the same educational
attainment and in jobs with similar schooling requirements
has ranged between 13% (Verdugo & Verdugo, 1989) to
20% or more (Chevalier, 2003; Robst, 2008). Similarly, the
overeducation penalty varies by gender, but the difference
is not significant (21.6% for men and 19.1% for women;
Robst, 2008).
Explanations for the causes of overeducation fall mainly
into three groups: (a) individuals become overeducated to
compensate for fewer advancement opportunities (the
advancement hypothesis; Buchel & Mertens, 2004), (b)
worker heterogeneity among college graduates (the heterogeneity hypothesis; Chevalier, 2003), and (c) oversupply of
low demand majors (Vedder et al., 2013). The heterogeneity hypothesis argues that as access to higher education
widens, lower ability students join higher ability students in
the college graduate pool (Chevalier, 2003). However,
research controlling for matched counterparts, including
gender and ability levels cannot explain the persistence of
overeducation in the data (McGuinness, 2003; McGuinness,
2006; Green, McIntosh, & Vignoles, 1999). Although some
evidence supports the advancement hypothesis, more recent
papers find evidence to support the signaling hypothesis.
The signaling hypothesis argues that a college degree has
lost its value as a signal device to prospective employers as
more students graduate with college degrees (Vedder et al.,
2013).
Finally, research findings are limited by the difficult of
properly measuring overeducation. Subjective and objective measures have been used to evaluate whether an individual is overeducated. Subjective measures estimated the
incidence from 17% to 32% (Chevalier, 2003). Objective
measures, such as the one used in this paper, found a
slightly wider incidence range 17% to 42% (Battu et al.,
2000, Daly et al., 2000; Duncan & Hoffman, 1981).
This article contributes to the literature on overeducation
by controlling for industry classifications and occupational
codes, and provides overeducation penalties estimates for
13 business-related majors by gender. The addition of
industry and occupational classifications allows us disentangle the overeducation estimates by business and industry
specific skills. It follows the preferred convention of measuring overeducation as more than one standard deviation
above modal educational attainment within the occupation.
Finally, we use the 2011 (annual) American Community
Survey (ACS; Ruggles et al., 2010) to better capture the
effect of the growth in college enrollment experienced in
the last decade.
MODEL
The dominant framework for explaining earnings distributions within developed economies was established by Gary
Becker (1964). The Mincerian earnings model (1974) later
provided the empirical structure to assess and measure
human capital. National interest in human capital accumulation is based on the economic theory that it leads to
increased productivity, income, and economic growth.
A direct measurement of human capital is not possible.
The established convention follows the Mincerian earnings
equation, which explains earnings as a function of human
capital accumulated through education and work experience:
Earnings D f ðEducation; ExperienceÞ:
Educational attainment is measured by years of education completed and by highest degree attained (high school,
college, graduate, and professional). This model assumes
college education adds to human capital by enhancing time
management, accountability, and critical thinking skills,
which in theory make an individual more productive in the
workplace. Yet, some college majors are more narrowly tailored in skill sets (such as Accounting compared to general
business or business management). Therefore, we control
for the different majors within business degrees.
Here we employ the objective measure of overeducation
(Battu et al., 2000). To derive the measure, we calculate
the required education level by position, and estimate overeducation with respect to the modal required educational
level for a given position:
ReqEdu (required education): modal educational attainment
plus or minus 1 standard deviation
Edu (matched-education): Edu D ReqEdu
OverEdu (overeducation): OverEdu D Edu–ReqEdu if
Edu > ReqEdu
UnderEdu (undereducation): UnderEdu D ReqEdu–Edu if
ReqEdu > Edu.
Following the model specified by Verdugo and Verdugo
(1998), the theoretical expression is as follows:
Wage Income D f Education; Experience; Major; Xij ;
Where Xij is a vector consisting of observable individual characteristics, such as race (Robst, 2001), marriage status, presence of children (Green et al., 2002),
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
OVEREDUCATION PENALTIES IN BUSINESS MAJORS
and gender (female) to capture the significant differences by gender for overeducation, especially in the presence of children under 18 years old (Green et al., 2002;
McGoldrick & Robst, 1996).
Interaction terms are added in the empirical equation to
isolate the effect of mismatched education to employment in
each of the business majors (Robst, 2001). We also construct
a proxy for years of work experience by subtracting the total
years of schooling from the individuals’ age. This proxy
introduces measurement error in the empirical model, but as
the proxy would overestimate the experience variable in the
likely presence of worker heterogeneity, its effect should
cancel out (Verhaest & Omey, 2012). In addition, veteran,
disability, federal government employee, and citizenship are
included to capture important characteristics, which affect a
worker’s wage rate. For control variables, we add state dummies to capture state fixed effects, as well as industry (16),
occupation (25), and metro area dummies.
The final expression for the model is the following:
Wage Income D f (Education, Experience, Business
Degree Majors, Female, Has Young Children, Marriage
Status, Race, Veteran, Disability, Federal Government
Employee, Citizenship, Metro, Occupation and Industry
Classifications).
DATA
The data were obtained from the 2011 annual ACS (Ruggles et al., 2010). The ACS is an annual and multiyear survey conducted by the U.S. Census Bureau. The sample
used in this study included individuals who had worked at
least 40 hr a week in the previous year and earned nonzero
wage income. We limited our analysis to individuals with a
bachelor degree and holding one of the 13 business-related
majors (see Table 1).
The summary statistics is based on weighted data. The
sample consists of 20,659,080 observations. The average
individual in this sample is 43 years old, has 21 years of
experience; 28.5% of surveyed individuals has kids younger than 13 years old; 60.7% are married; 77.3% are White,
6.6% are Hispanic, 6.6% are Black; 45% are women; and
81% live in a metropolitan area. Among all individuals surveyed, 20.25% have a bachelor’s degree (highest attained
educational level). In this study, we define an individual to
be overeducated for his or her position if workers in the
same position are more than one standard deviation from
the mode. According to this definition, among all individuals who have attained a bachelor’s degree, 5.7% are said to
be overeducated for their position. Among the control variables, the dataset includes all 50 states, 16 industries, and
25 occupational codes. We coded variable has young children to equal one if the individual has children younger
than 13 years old. Among Asian individuals (race) we
121
TABLE 1
Sociodemographic Summary Statistics (Weighted Averages)
Variable
Wage income (U$)
Age (years)
Experience (years)
Has small children
Married
Divorced
White
Black
Asians
Hispanics
Native Americans
Other Asians
Other Races
Foreign born
Female
Male
Metro area
Veteran
Disability
Federal government employee
Average
Minimum
Maximum
$70,767.42
41.92
19.95
29.41%
62.86%
9.51%
74.98%
8.01%
3.80%
7.35%
0.34%
4.08%
1.45%
13.44%
44.86%
55.14%
84.82%
6.41%
2.94%
4.89%
$4
19
0
$607,000
94
72
included Chinese, Indian, and Korean individuals. Other
Asian individuals includes all other Asian ethnic groups.
We also control for the presence of federal government
employees, those with disabilities, veterans, and living in
metropolitan areas.
We analyze wage differential data for 13 business-related
majors by gender; to our knowledge, this is the most detailed
research on overeducation of this type. We coded the following majors: economics, general business, accounting,
actuarial science, business and management administration,
operations logistic and e-commerce, business economics,
marketing and marketing research, finance, human resources
and personnel management, international business, hospitality management, management information system, and miscellaneous business administration (see Table 2).
The modal wage income by majors range $30,000 (hospitality) to $100,000 (finance and economics). In the sample, 4.7% of individuals with a bachelor degree are
overeducated for their position. The incidence of overeducation among business-related majors is less than doubled
(7.2%). Women in business-related majors make up about
half (3%), and they major mostly in business management,
general business, marketing, and accounting. The top four
majors among all overeducated individuals in this sample
were business management, general business, accounting,
and marketing. The major with the least overeducated
major was actuarial science.
METHODOLOGY
Since we are interested in the penalty from overeducation
on wage income growth, we estimated the following
122
I. LI ET AL.
TABLE 2
Summary Statistics of Business-Related Majors by Gender
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Business-related
majors
Economics
General business
Accounting
Actuarial science
Business management
Operations and logistics
Business economics
Marketing
Finance
Human resources
International business
Hospitality
Management information system
Miscellaneous business
Total
Number of
overeducated workers:
Both genders
Overeducated males
Overeducated females
Proportion of
overeducated
women to men
90,086
329,407
176,819
649
523,654
11,565
8,164
155,914
97,939
28,869
14,230
35,612
20,558
19,087
1,512,552
61,688
206,901
89,902
225
304,333
8,439
6,710
78,424
65,144
12,815
7,578
16,839
13,639
10,505
883,143
28,398
122,506
86,917
424
219,321
3,126
1,454
77,490
32,794
16,054
6,652
18,773
6,919
8,582
629410
0.46
0.59
0.97
1.88
0.72
0.37
0.22
0.99
0.50
1.25
0.88
1.11
0.51
0.82
0.71
Wage income
(mode)
100000
40000
50000
60000
50000
60000
60000
50000
100000
50000
50000
30000
80000
40000
Note: The sample (weighted) includes only individuals who worked full time (usual work hours of 40 or more per week), earned nonzero wage income,
and whose highest educational attainment includes only a bachelor’s degree.
empirical equation:
LnðWageIncomeÞ D B0 C B1 Age C B2 Experience
C B3 Has Young Kids C B4 Divorced C B5 Blacks
C B6 Asians C B7 Hispanics C B8 Native Americans
C B9 OtherAsians C B10 Other Races C B11 Foreign Born
C B12 Female C B13 Metro Area C Bð14 ¡ 27Þ Major OverEdu
C Bð28 » 41Þ Major OverEdu Female C B42 Veteran
C B43 Disability C B44 Federal Government Employee
C Bð45 ¡ 94Þ State Dummies C Bð95 ¡ 110Þ Industry Dummies
C Bð111 ¡ 135Þ Occupation Dummies:
We estimated two sets of regressions: (a) the benchmark linear model (with and without the full set of controls) using ordinary least squares (OLS) with robust
standard errors; and (b) a two-stage least-squared
method (2SLS; with and without the full set of controls). The predicted variable is log of wage income.
The predictor variables include standard socioeconomic
measures (SEM), indicator variables for individuals who
are overeducated for their position by their college
major, interaction term of the female variable with each
of the indicator variables for overeducated individuals
by major, and other control variables (states, industries,
and occupations). Omitted in the equation, and therefore
serving as benchmarks, are variables White (race) and
male (gender), non–foreign born, Hawaii (state), mining
(industry), and active military (occupation). We used
Stata (ver. 13, StataCorp LP, College Station, TX) to
run the regressions.
RESULTS AND DISCUSSIONS
We discuss the results of the 2SLS regressions because
OLS estimates are biased in the presence of endogeneity.
The set of results from the 2SLS regression controls for
state of residence, industries, occupations, and other socioeconomic background. The overall fit of the model is .29
(R2). We estimated the coefficients with robust standard
errors. The results of the full regressions are available upon
request. Among the overeducated business-related majors,
the penalties range from 4.4% (general business) to 13.8%
(accounting). Two majors enjoyed a premium for overeducation (actuarial sciences and operations logistics). The
estimated coefficients were significantly different from
zero for economics (–12%), general business (–4.4%), business management (–7.7%), marketing (–5.7%), hospitality
(¡8.4%), and human resources (–11%). These values fall
within the range established by previous research (see
Table 3).
Ceteris paribus, females experience a 17.17% lower wage
relative to men. However, among the overeducated in business-related majors (excluding actuarial science, international business, and general business), women in
accounting, business management, logistics, and business
economics realized a smaller wage penalty. Once we collect
the coefficients for (a) overeducation by major and gender,
(b) women, (c) overeducation by majors, the penalty from
overeducation is reduced for some, and increased for others.
Despite the overeducation premium for all actuarial science
majors, once gender is considered, an overeducated female
actuarial science major enjoys only a 2% premium over all
other majors and male individuals. Conversely, women in
OVEREDUCATION PENALTIES IN BUSINESS MAJORS
123
TABLE 3
Summary Regression Results
OLS
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Explanatory variables
Age
Experience
Has small children
Divorced
Black
Asians
Hispanics
Native American
Other Asians
Other races
Foreign born
Female
Metro
2SLS
Coefficient Robust SE Coefficient Robust SE Coefficient Robust SE Coefficient Robust SE
0.0155 **
0.2301 **
¡0.0351 **
¡0.1868 **
0.0577 **
¡0.1618 **
¡0.1384 **
¡0.0989 **
¡0.0925 **
¡0.0900 **
¡0.2709 **
0.2739 **
0.0001
0.0032
0.0052
0.0055
0.0090
0.0061
0.0203
0.0089
0.0124
0.0060
0.0031
0.0036
0.0142 **
0.2053 **
¡0.0295 **
¡0.1424 **
¡0.0521 **
¡0.1376 **
¡0.0614 **
¡0.1355 **
¡0.0976 **
¡0.1000 **
¡0.1804 **
0.1563 **
0.0001
0.0030
0.0048
0.0053
0.0084
0.0058
0.0188
0.0084
0.0114
0.0055
0.0031
0.0038
¡0.0156 **
0.0113 **
0.1309 **
0.0034
¡0.2125 **
0.0171 *
¡0.2247 **
¡0.1648 **
¡0.1437 **
¡0.1500 **
¡0.0498 **
¡0.2607 **
0.2648 **
0.0001
0.0008
0.0076
0.0058
0.0058
0.0094
0.0073
0.0203
0.0094
0.0130
0.0065
0.0030
0.0037
¡0.0143 **
0.0100 **
0.1179 **
0.0044
¡0.1652 **
¡0.0883 **
¡0.1931 **
¡0.0847 **
¡0.1752 **
¡0.1486 **
¡0.0643 **
¡0.1716 **
0.1484 **
0.0001
0.0007
0.0072
0.0054
0.0055
0.0088
0.0070
0.0189
0.0089
0.0120
0.0060
0.0031
0.0038
¡0.3313 **
¡0.2622 **
¡0.3725 **
0.6849 **
¡0.2827 **
¡0.1678 **
¡0.2770 **
¡0.2768 **
¡0.2192 **
¡0.3409 **
¡0.2316 **
¡0.4487 **
¡0.1541 **
¡0.2545 **
0.0276
0.0160
0.0232
0.2199
0.0113
0.0538
0.0845
0.0249
0.0280
0.0495
0.0796
0.0499
0.0529
0.0526
¡0.1204 **
¡0.0436 **
¡0.1382 **
0.8253 **
¡0.0776 **
0.0396
¡0.0822
¡0.0561 *
¡0.0288
¡0.1092 *
0.0093
¡0.0783
0.0109
¡0.0194
0.0269
0.0156
0.0223
0.2600
0.0112
0.0491
0.0778
0.0244
0.0260
0.0478
0.0761
0.0503
0.0541
0.0532
¡0.3307 **
¡0.2623 **
¡0.3722 **
0.6847 **
¡0.2821 **
¡0.1670 **
¡0.2764 **
¡0.2774 **
¡0.2169 **
¡0.3402 **
¡0.2300 **
¡0.4575 **
¡0.1530 **
¡0.2531 **
0.0277
0.0160
0.0232
0.2201
0.0113
0.0538
0.0845
0.0249
0.0280
0.0495
0.0797
0.0499
0.0529
0.0526
¡0.1196 **
¡0.0435 **
¡0.1376 **
0.8254 **
¡0.0769 **
0.0405
¡0.0813
¡0.0565 **
¡0.0265
¡0.1084 **
0.0108
¡0.0840 y
0.0120
¡0.0181
0.0269
0.0156
0.0223
0.2599
0.0112
0.0492
0.0778
0.0243
0.0260
0.0478
0.0761
0.0500
0.0541
0.0532
0.1224 *
0.0274
0.1022 **
¡0.7349 *
0.0688 **
0.3392 **
0.4424 **
0.1539 **
0.0244
0.0922
0.0724
0.1863 **
0.1127
0.0777
¡0.0941 **
¡0.2057 **
0.1588 **
10.4908 **
No
No
No
200,817
0
0.1619
0.0490
0.0249
0.0319
0.3121
0.0161
0.0964
0.1313
0.0326
0.0428
0.0674
0.1089
0.0660
0.0836
0.0771
0.0065
0.0091
0.0055
0.0048
0.0723
¡0.0062
0.0698 *
¡0.6763 *
0.0453 **
0.1805 *
0.3062 *
0.1110 **
¡0.0080
0.0675
¡0.0129
0.1264 *
0.0835
0.0264
¡0.0829 **
¡0.1633 **
0.2005 **
11.1441 **
Yes
Yes
Yes
200,817
0
0.2892
0.0472
0.0238
0.0307
0.3287
0.0156
0.0900
0.1224
0.0317
0.0402
0.0653
0.1027
0.0679
0.0824
0.0796
0.0061
0.0084
0.0063
0.0253
0.1225 **
0.0273 **
0.1027
¡0.7340 **
0.0680 **
0.3394 **
0.4437 **
0.1539 **
0.0220
0.0925
0.0725
0.1914 **
0.1123
0.079
0.0490
0.0249
0.0319
0.3127
0.0161
0.0964
0.1313
0.0326
0.0427
0.0674
0.1089
0.0661
0.0836
0.0771
0.0723
¡0.0068 **
0.0699 **
¡0.6759 **
0.0441 **
0.1805 **
0.3074 **
0.1105
¡0.0100
0.0676
¡0.0127 y
0.1310
0.0833
0.0257
0.0472
0.0307
0.3287
0.0157
0.0901
0.1224
0.0316
0.0401
0.0653
0.1028
0.0676
0.0824
0.0796
0.0238
¡0.1446 **
0.1339 **
10.4260 **
No
No
No
201,074
0
0.1631
0.0102
0.0055
0.0205
¡0.1094 **
0.1783 **
10.5194 **
Yes
Yes
Yes
201,074
0
0.2902
0.0094
0.0063
0.0322
Overeducated by major
Economics OE
General business OE
Accounting OE
Actuarial science OE
Business management and administration OE
Operations logistics and e-commerce OE
Business economics OE
Marketing OE
Finance OE
HR and personnel management OE
International business OE
Hospitality management OE
Management information system OE
Miscellaneous business administration OE
Overeducated by major interacted with female
Economics OE*Female
General business OE*Female
Accounting OE*Female
Actuarial science OE*Female
Business management and administration OE*Female
Operations logistics and e-commerce OE*Female
Business economics OE*Female
Marketing OE*Female
Finance OE*Female
HR and personnel management OE*Female
International business OE*Female
Hospitality management OE*Female
Management information system OE*Female
Miscellaneous business administration OE*Female
Veteran
Disability
Federal government employee
Constant
Industry fixed effect
Occupation fixed effect
State fixed effect
Observations
Prob>F
R2
Note: The sample data include only individuals who were sampled in the annual ACS 2011 survey, worked full time, earned nonzero income, and had only
a bachelor’s degree. The regressions are unweighted.
y
p .10. *p .05. **p .01.
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
124
I. LI ET AL.
accounting, economics, general business, business management, finance, and human resources all suffer a net wage
penalty of approximately 20% relative to all men and
majors. There are two exceptions that are significant and
positive: females who are overeducated for their positions
and majored in operations and logistics and business economics enjoy a net premium of 5% and 5.4%, respectively,
over all men and other majors (see Table 4).
Other variables that are significant in the results are disability (penalty of 11%), and federal government employee
(premium of 17.8%).
Once industry and occupation classifications are controlled (albeit a rough approximation), the wage penalties
for overeducated business majors are significantly reduced
(compared to previous literature and regression results without controls). In general, the estimates of overeducation are
within the range found in the literature but narrower. However, the extent of the impact is not homogenous across business majors, especially for women. One potential
explanation for the unequal impact by major is the inability
of college graduates to clearly signal potential productivity
due to pooling in the labor market. Another possible explanation is the specialized nature of some business majors,
such as actuarial science and accounting, which require
licensure to practice the profession as actuary and certified
public accountant. Licensing exams may serve as a screening mechanism, which eliminates weaker candidates from
seeking the degree, therefore thinning the lower end of the
abilities distribution tail. Interestingly, overeducated women
who majored in business economics and operations logistics
do not suffer any wage penalties but rather enjoy a premium
for overeducation compared to men and all other majors
(which suggests need for further investigation). Finally,
females in most business majors suffer a lower wage penalty
TABLE 4
Summary of Wage Penalties for Overeducated Business Related
Majors by Gender
Business related majors
Economics
General business
Accounting
Actuarial science
Business management
Operations and logistics
Business economics
Marketing
Finance
Human resources
International business
Hospitality
Management information system
Miscellaneous business
Men
Women
¡11.96*
¡4.35
¡13.76*
82.54
¡7.69
4.05
¡8.13
¡5.65
¡2.65
¡10.84*
1.08
¡8.40
1.20
¡1.81
¡21.89
¡22.18*
¡23.93*
¡2.21*
¡20.45*
4.95*
5.44*
¡11.76
¡20.81
¡21.24
¡17.36*
¡12.46
¡7.63
¡16.40
Note: Wage differential (in percent) relative to all other majors. *Significant at 10% level.
for being overeducated compared to overeducated males in
the same business-related majors.
CONCLUSION AND IMPLICATIONS
Overeducation and the associated wage penalty have been
studied extensively. The literature finds that the individuals
who are overeducated in fields that were not related to their
studies were subjected to three times the penalty of those
whose degree majors were related (Chevalier, 2003). However, among business-related majors, overeducation carries
dissimilar wage penalties. Further, when gender is taken into
account, female workers who are overeducated in a business-related major suffer a lower wage penalty relative to
females in other majors. Two majors in this paper are of particular interest: women overeducated in operations logistics
and business economics majors enjoyed a net premium of
about 5%.
This paper examined ACS data for business-related
majors to answer the question of whether overeducation
wage penalty is still present and whether gender has an additional impact on the penalty. The results for some of the 13
overeducated business-related majors were not significantly
different from zero. For most majors, the penalty was narrower than found in previous literature. There is heterogeneity across business-related majors, with actuarial science,
business economics, and operation logistics showing unusual
values. The implication of the results suggest that while overeducation in general penalizes the worker for a given position, females in business and related majors are not penalized
as heavily compared to females in other majors who are overeducated for their positions. Future researchers should consider additional control variables for detailed occupational
ranks as well as a more precise measure of years for work
experience for more robust results.
REFERENCES
Battu, H., Belfield, C., & Sloane, P. (2000). How well can we measure
graduate over education and its effects? National Institute Economic
Review, 171, 82–93.
Battu, H., & Sloane, P. J. (2002). To what extent are ethnic minorities in
Britain overeducated. International Journal of Manpower, 23, 192–207.
Becker, G. (1964). Human capital: A theoretical and empirical analysis
with special reference to education. New York, NY: Columbia University Press.
Buchel, F., & Battu, H. (2003). The theory of differential over-qualification: Does it work? Scottish Journal of Political Economy, 50, 1–16.
Buchel, F., & Mertens, A. (2004). Over-education, undereducation, and the
theory of career mobility. Applied Economics, 36, 803–816.
Carnavale, A. P., Strohl, J., & Melton, M. (2011). What’s it worth? The
economic value of college majors. Washington, DC: Georgetown University Center on Education and the Workforce.
Chevalier, A. (2003). Measuring over-education. Economica, 70, 509–531.
Chevalier, A., & Lindley, J. (2009). Over-education and the skills of UK
graduates. Journal of the Royal Statistical Society, 172, 307–337.
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:12 11 January 2016
OVEREDUCATION PENALTIES IN BUSINESS MAJORS
Daly, M. C., Buchel, F., & Duncan, G. J. (2000). Premiums and penalties
for surplus and deficit education: Evidence from the United States and
Germany. Economics of Education Review, 19, 169–178.
Duncan, J., & Hoffman, S. (1981). The incidence and wage effects of overeducation. Economics of Education Review, 1, 75–86.
Green, F., & McIntosh, S. (2007). Is there genuine underutilization of skills
amongst the over-qualified. Applied Economics, 39, 427–439.
Green, F., McIntosh, S., & Vignoles, A. (1999). Over-education and skills:
Clarifying the concepts. London, England: Center for Economic
Performance.
Green, F., McIntosh, S., & Vignoles, A. (2002). The utilization of education and skills: Evidence from Britain. The Manchester School, 70(60),
792–811.
Hecker, D. E. (1992). Reconciling conflicting data on jobs for college graduates. Monthly Labor Review, 115, 3–12.
Katz, L., & Murphy, K. M. (1992). Changes in relative wages, 1963–1987: Supply and demand factors. The Quarterly Journal of Economics, 107, 35–78.
McGoldrick, K., & Robst, J. (1996). Gender differences in over-education:
A test of the theory of differential over-qualification. The American Economic Review, 86(2), 280–284.
McGuinness, S. (2003). Graduate over-education as a Sheepskin effect:
Evidence from Northern Ireland. Applied Economics, 35, 597–608.
McGuinness, S. (2006). Over-education in the labour market. Journal of
Economic Surveys, 20, 387–418.
National Center for Education Statistics. (2014). The condition of education 2014 (NCES 2014-083), annual earnings of young adults. Washington, DC: National Center for Education Statistics, U.S. Department of
Education.
125
Robst, J. (2001). Post school investment and wage differntials: Some further evidence. Southern Economic Journal, 121–132.
Robst, J. (2008). Over-education and college major: Expanding the definition of mismatch between schooling and jobs. The Manchester School,
76, 349–368.
Ruggles, S., Alexander, J. T., Genadek, K., Goeken, R., Schroeder, M. B.,
& Sobek, M. (2011). Integrated public use microdata series: Version
5.0 [Machine readable database]. Minneapolis, MN: Minnesota Population Center.
Sloane, P., Battu, H., & Seaman, P. (1996). Over-education and the formal
education/experience and training trade-off. Applied Economics Letters,
3, 511–515.
Sloane, P., Battu, H., & Seaman, P. (1999). Over-education, undereducation and the Brittish labour market. Applied Economics, 31,
1437–1453.
Verdugo, R. R., & Turner Verdugo, N. (1988). The impact of surplus
schooling on earnings. The Journal of Human Resources, 24,
629–643.
Verhaest, D., & Omey, E. (2012). Over-education, undereducation and
earnings: Further evidence on the importance of ability and measurement bias. Journal of Labor Resources, 33, 76–90.
Vedder, R., Denhart, C., & Robe, J. (2013, January 24). Why are recent
college graduates underemployed? University enrollments and labor
market realities. Center for College Affordability and Productivity.
Retrieved from http://centerforcollegeaffordability.org/uploads/
Underemployed%20Report%202.pdf
Weisbrod, B. A. (1964). External benefits of public education: An economic analysis. Princeton, NJ: Princeton University.