The economic reward for studying economics.

Black, Dan A. ; Sanders, Seth ; Taylor, Lowell 等

I. INTRODUCTION

Each year, college students must select a major from a bewildering array of choices. Academic advisors extol the aesthetic beauty, social value, intellectual rigor, and inherent interest of the various disciplines, and they emphasize the value of their own department's major as preparation for students' future career paths. Economists in particular point to the economics major as providing solid grounding for a variety of real-world careers--from public policy analyst to executive--and as a good background for further graduate work in economics, business, and law.

The information we pass on to our students is based largely on a collection of anecdotes, and this advice is often met with well-founded skepticism. Students and parents understand that academic advice often comes from individuals who have an incentive to steer students toward majoring in their own departments. Little concrete information is provided about the actual economic success of graduates based on their choice of college major.

In this article, we use a unique data set of over 85,000 college-educated respondents to calculate the wages of college graduates by their major. We focus on two broad issues. First, we consider college graduates who do not pursue post bachelor's education. We ask how the-wages of the economics majors compare to demographically similar individuals who choose other college majors. We also provide evidence about the occupations held by economics majors. Second, we consider evidence about graduate-level education for economics majors. We find that over 40% of economics majors have earned graduate degrees, most commonly in business or law. We examine the wages of workers who have obtained a master's degree in business or a law degree and ask how wages of the economics undergraduate majors differ from those of other majors.

Our results can be summarized as follows: Among college graduates with no advanced degrees, economics majors generally fare well, earning significantly more than graduates with the most popular major, business administration, and more than other social science majors, humanities majors, and arts majors. Only engineering majors earn significantly more than economics majors. Among individuals who pursue a master's degree in business or a professional degree in law, those who have an undergraduate economics major generally earn more than individuals with other majors (the only exception being the chemical engineering major as a background for the MBA).

II. DATA DESCRIPTION AND METHODOLOGY

We use the 1993 National Survey of College Graduates (NSCG) to examine the college majors of full-time workers. The NSCG stems from an initiative of the National Science Foundation (NSF) that compiled information on scientists and engineers in the United States. The NSF and the Bureau of the Census conducted a survey based on the 1990 Decennial Census Long Form sampling frame, with the sample limited to those with at least a baccalaureate degree and who were age 72 or younger as of 1 April 1990. The Census Bureau drew a stratified sample of 214,643 respondents, first contacting individuals with a mail survey and then, if necessary, with a telephone or in-person interview. In the collection of these data, a great deal of attention was paid to the accuracy of the education responses, and detailed information was gathered about the majors of the respondents for up to three degrees.

From the original 1990 sample, a few members had emigrated from the United States (2,132), died (2,407), were institutionalized (157), or were over 75 years old (211) and were hence out of the survey's scope. Surprisingly, 14,319 respondents were also excluded from the sample because they had no four-year college degree despite reporting a four-year degree on the 1990 Census. (1) Another 46,487 declined to participate. (2) This results in a (weighted) response rate of 80%, or a sample of 148,928 respondents. We are interested in individuals who were age 25 to 55 in the 1990 Census (which reduces the number of observations by 28,656); who had nonimputed gender, race, age, and ethnicity (which reduces the number of observations by 2,417); who worked at least 1,365 hours in 1989 and had positive, nonimputed income, weeks worked, and hours per week (which reduces the number of observations by another 29,583). In addition, another 2,167 respondents reported no bachelor's degree or major, and we dropped these respo ndents from the sample, resulting in a working sample of 86,105 respondents. The NSCG provides over 140 different majors, which we aggregate to roughly 85 majors to avoid small cell sizes. (3) In an appendix table we provide a count of the undergraduate majors of the 86,105 respondents. There are over 1,700 economics majors, but other majors are also well represented. The smallest major is ethnic studies, and the largest is business administration.

Because the sampling frame of the NSCG is the 1990 Census, anyone not having a degree by 1990 would not be included in the sample. As a result, we restrict the sample to those at least 25 years of age (in 1990) to ensure that most individuals would have had the opportunity to complete their undergraduate education. Similarly, to avoid any complications with differential retirement ages, we restrict the sample to workers age 55 and under.

In our discussion of wage differences by college major, we report estimates from the regression model

(1) 1n([y.sub.i]) = g([X.sub.i]) + [M.sup.T][beta] + [u.sub.i],

where 1n([y.sub.i]) is a the natural log of the ith worker's wage; [X.sub.i] is a vector of demographic covariates that includes age, race, and ethnicity (Asian, black, nonblack Hispanic, Native American, and non-Hispanic white) and gender; M is a vector of education covariates (e.g., college majors), [beta] indicates the parameters of interest; and [u.sub.i] is an error term. (4) As is standard practice when using census data, we calculate the wage as the earnings last year divided by the product of weeks worked last year and hours worked per week. Importantly, compared to Public Use Microdata Sample, our measure of earnings is top coded at $999,999 rather than $150,000. The NSCG data also contain a measure of 1993 earnings, but this measure is top coded at $150,000. We prefer the census measure because it allows us to better estimate the returns to lucrative majors and occupations. (5)

We do not specify the function g([X.sub.i]) parametrically. Rather, we demean the data for each point, or cell, in X. We then estimate the impact of each major by regressing the demeaned wage data on the vector of college majors. (6) Thus, identification of the wage differential associated with college majors comes from comparing workers with identical demographic characteristics (say, non-black Hispanic females aged 32) but with differing majors. We estimate the model with Stata statistical software and report robust standard errors allowing the variances to differ at each cell using Stata's cluster command. This accounts for the heteroscedasticity associated with difference in cell sizes. Finally, all estimations weights to account for the NSCG's sample stratification.

To implement the model, we must of course exclude one of the 85 majors. We use economics majors as the excluded group, because this facilitates easy comparison with other majors.

III. WHAT DO ECONOMICS MAJORS DO, AND HOW MUCH DO THEY EARN?

Our first set of analyses is of full-time workers who have earned a bachelor's degree but no further graduate degrees. As one would expect, among the college educated, the undergraduate major is a significant determinant of an individual's occupation. Table 1 provides a list of the most common occupations for economics majors and five additional majors in 1990: two other popular social science majors, history and political science; two professional business majors, accounting and business administration; and for contrast, electrical engineering. The table lists all occupations that employ at least 3% of one of the six listed majors.

We notice that the electrical engineering majors are, not surprisingly, highly concentrated in engineering occupations (with more than 53% working in computer science or engineering). Similarly, 49% of accounting majors work as accountants. By way of comparison, the social science and business administration majors are distributed broadly over a variety of occupations. The occupation distribution of economics and business administration majors are similar, with concentrations of employment in top- and midlevel management positions, accounting and financial specialties, and sales. The political science and history majors are more likely to be elementary or secondary school teachers.

In Table 2 we present estimates from our wage regression. This specification is restricted to college graduates who have no advanced degree, a sample of 55,422 workers, or about 64% of the sample. We include all majors in our estimation but report only the estimated coefficients for majors for which we have 350 or more observations.

Economics majors fare quite well in the labor market. Majors in other social sciences earn at least 13% less than demographically comparable economics majors. Similarly, education, humanities, and arts majors earn substantially less than economics majors. Business administration majors earn 11% less than economics majors, as do majors in a variety of other "professional degree" programs. Wages of economics majors are comparable to those of "hard science" majors, such as chemistry, math, and physics, and are similar also to accounting and finance majors. (7) Among the majors listed, only engineering majors earn substantially more than economics majors. (8)

In Table 3, we stratify our sample of bachelor's only respondents by gender in the first two columns and by broad age categories (25 to 34, 35 to 44, and 45 to 55 years) in the last three columns. To avoid the imprecision associated with small cells, we report only selected majors that have at least 100 observations. Economics is seen to be a lucrative major for both men and women. (9) Some interesting patterns emerge in the examination of age cohorts. The "penalty" (relative to the economics major) for majoring in elementary education, history, English, and foreign languages appears to have increased for the younger cohort. We notice that the "premium" earned by computer science majors is driven primarily by the relatively high wages of young workers. (10) The major finding is that the economics was a lucrative field for each cohort.

Finally, we provide some evidence about the distributions of returns across majors. For the six majors listed in Table 1, we estimated the returns using quantile regressions for each decile. (11) This allows us to examine how, say, majoring in electrical engineering relative to economics affects earnings at the 20th percentile of the wage distribution. We depict the results in Figure 1.

For business administration, political science, and history, the wage gap is relatively constant across the wage distribution, roughly ranging from -20% to -10%. In contrast, there are substantial differences across wage distributions for electrical engineering and accounting. Accounting majors at the 10th percentile earn 10.7% more than economists, but at the 90th percentile accounting majors earn 11.4% less than economics majors. At the median, there is virtually no difference between the earnings of the two majors (with accounting majors earning only 0.7% less than economics majors). The results for electrical engineering majors are more striking. At the 10th percentile of earnings, electrical engineering majors earn 40.7% more than economics majors, but at the 90th percentile of earnings, the electrical engineering majors actually earn 11.4% less than the economics majors. Interestingly, the median estimate (a 17.0% gap) is quite close to the ordinary least squares (OLS) gap (a 15.9% gap). (12) For both a ccounting and electrical engineering, the gap declines quite smoothly, but clearly this figure also presents a stunning rejection of the constant impact of college major across the wage distribution. Our exercise also illustrates the value of avoiding overly restrictive assumptions about the functional form of the regression equation.

IV. THE ECONOMICS MAJOR AS A PREPARATION FOR GRADUATE SCHOOL

Within each major, a substantial fraction of college graduates pursue advanced degrees. In Table 4 we confirm that the rate at which students attend graduate education differs substantially by undergraduate major. (13) (We restrict attention to workers who are old enough that they have likely completed their graduate education--respondents aged 35 to 55.) For example, about two-thirds of physics majors earn an advanced degree, including 32% who earn a Ph.D. As an extreme contrast, fewer than 20% of business administration majors pursue any graduate degree. Economics majors are somewhere in the middle of the pack; about 45% of economics majors pursue graduate degrees. These differences are not the result of demographic differences across majors; when we estimate a logit model controlling for age, race-ethnicity, and gender, these general patterns persist.

Table 5 provides a more detailed breakdown of the highest degree earned by undergraduate economics majors and by students in three comparison majors: electrical engineering, history, and political science. Among electrical engineering majors, those who pursue graduate study are most likely to stay in their own discipline, with a modest number also entering MBA programs. The social science majors, in contrast, are quite likely to enter graduate programs in disciplines that differ from their undergraduate major, especially education (for history majors), business, and law. The fraction of political science majors who enter law is particularly striking.

How does the undergraduate major affect earnings after graduate school? Tables 6 and 7 allow an evaluation of a claim often heard in economic advisors' offices--that economics is good preparation for a subsequent MBA or J.D. degree. The NSCG data contain 4,012 respondents who have a master's degree in a business discipline. In Table 6, we report the results from estimating our wage regression using a sample of workers who have a master's degree in a business discipline but conditioning on undergraduate majors. We estimate coefficients for 84 different undergraduate majors (no audio-speech therapy major in the sample received an MBA), but report results only for majors in which there are at least 30 individuals. The relative success of economics majors among this group is clear. Among undergraduates who pursue MBAs, economics majors rank second in terms of wages out of the 23 most common prebusiness majors. Economics majors trail only chemical engineering majors. Other majors earn less, though the differences in many cases are not statistically significant.

Table 7 provides results for an analogous exercise among individuals who attend law school. Although we consider all 78 majors for which someone attends law school, we report only those majors that have at least 30 people in our sample receiving law degrees. We find that among the law school graduates previously receiving one of the 12 most common prelaw majors, economics majors have the highest wages.

V. CONCLUDING REMARKS

Readers who look at our tables will no doubt form a variety of hypotheses of their own for explaining the observed wage differentials. There are likely at least three elements at work.

The first factor concerns the value added of the degree programs themselves. In a good undergraduate economics program, students develop an ability to think critically: They gain broadly applicable analytic and quantitative skills that improve decision making in a wide range of tasks. In short, it may be that economics majors are better trained than many other majors in skills that have high returns in the marketplace.

A second factor is compensating differentials. Many artists and musicians cannot imagine spending their life doing anything other than pursuing their art, and the relevant marginal student (say, a student choosing between studying economics or music performance with the bassoon) may be willing to "pay" for the privilege of following her passion in the form of lower lifetime earnings. Because college major and occupation are correlated, these compensating differentials will appear in the form of varying returns to college major.

Finally, there is certainly a considerable amount of self-selection across majors in terms of innate ability--intelligence, talent, ambition, and drive. The large difference in wages between, say, undergraduate physics majors and most undergraduate social science majors might stem in large measure from the fact that students who tackle the traditionally challenging physics major are generally among the smartest and most intellectually ambitious students. To the extent that such sorting does occur, college major may serve as a useful signal to employers, even in cases in which the skills acquired in the undergraduate program are largely irrelevant to the job for which an individual is being considered. Such signaling would only serve to reinforce the tendency of talented students to select challenging majors.

There are patterns that seem explicable only by such selection. The low rate of return of business majors who pursue MBAs (relative to economics majors who also earn MBAs) is difficult to explain unless there is substantial selection into undergraduate business majors. Similarly, the low rates of return to an undergraduate science major are difficult to understand unless one looks at the rate at which these students pursue advanced degrees.

Selection is probably not the whole story, however. For example, on average, physics and math majors are probably at least as smart and hard working as engineering students. Indeed, among high school seniors in 2001, Scholastic Aptitude Test scores (math/verbal) are higher among those intending to major in mathematics (625/549) and physical sciences (588/568) than among those planning to major in engineering (572/523). (14) The relatively high wages of engineering majors likely stem from the highly valued skills that these students acquire in their degree programs. This brings us back to the first of our explanations about the value of majors--that the measured differentials reflect in part real differences in the market returns to the field of study. If so, our article provides evidence that there is indeed a substantial economic reward to studying economics.

APPENDIX TABLE A1

Counts of Undergraduate College Majors, Full-time Workers Aged 25 to 55,
1993 National Survey of

National Survey of College Graduates

Major Count

Physics 1,157
Chemistry 2,236
Geology 703
Earth sciences 358
Biochemistry & biophysics 340
General biology 2,999
Botany & plant science 318
Micro- & molecular biology 428
Nutrition 248
Zoology 476
Other biology 469
Applied math 400
Math 1,935
Statistics & other math 308
Aerospace engineering 577
Architecture 243
Civil engineering 2,414
Chemical engineering 1,455
Electrical engineering 4,174
Science engineering 263
General engineering 211
Industrial engineering 569
Mechanical engineering 3,112
Mineral & materials engineering 328
Other engineering 580
Electrical technology 642
Industrial technology 381
Mechanical technology 434
Other technology 268
Computer science 2,840
Other computer science 348
Education--administration 208
Education--science 559
Education--elementary 3,709
Education--physical 1,193
Education--secondary 1,399
Education--special 525
Education--social science 275
Education--other 1,889
Audio-speech therapy 249
Premedicine 547
Premedicine 547
Medicine 220
Nursing 1,496
Pharmacy 413
Physical therapy 249
Other health 505
Agricultural business 324
Animal science 272
Other agricultural science 167
Agricultural architecture & environmental 787
Accounting 3,357
Business administration 6,181
Marketing 1,674
Finance 912
Other business 861
Criminology 759
Journalism 630
Communication 1,084
Public administration 256
Social work 1,032
Home economics 685
Leisure 419
Conservation 640
Anthropology 290
Economics 1,789
English 2,474
Ethnic studies 137
Foreign languages 1,073
Geography 266
History 2,036
Liberal arts 794
Philosophy 948
Political science 1,930
Prelaw 195
Clinical psychology 421
General psychology 2,212
Other psychology 794
Sociology 1,942
Other social sciences 556
Drama 266
Fine arts 1,066
Music 688
Other arts 331
Other 515

Source: Authors' calculation, 1993 NSCG.

Note: Sample weights are not used for these calculations.

[FIGURE 1 OMITTED]

TABLE 1

1990 Occupational Distribution for Six Undergraduate Majors, Individuals
Aged 25 to 55, 1993 NSCG


Occupation Economics Accounting

Administrators and officials, 1.32 1.15
public administration
Financial managers 3.63 9.53
Managers, marketing, advertising, 4.40 0.84
& public relations
Managers and administrators, n.e.c. 14.23 7.87
Accountants and auditors 5.02 48.83
Other financial officers 4.88 3.94
Aerospace engineers 0.12 0.02
Electrical and electronic engineers 0.35 0.11
Engineers, n.e.c. 0.32 0.07
Computer systems analysts and 2.04 0.62
scientists
Teachers, elementary school 0.83 0.37
Teachers, secondary school 0.50 0.10
Electrical and electronic 0.11 0.04
technicians
Supervisors and proprietors, sales 7.51 2.27
occupations
Real estate sales occupations 3.34 0.81
Sales rep., mining, manufacturing, 5.51 0.81
& wholesale

Total 52.79 77.38

 Business Electrical
Occupation Administration Engineering

Administrators and officials, 1.55 0.12
public administration
Financial managers 2.70 0.11
Managers, marketing, advertising, 2.46 1.45
& public relations
Managers and administrators, n.e.c. 15.70 14.22
Accountants and auditors 4.48 0.00
Other financial officers 3.24 0.58
Aerospace engineers 0.11 3.28
Electrical and electronic engineers 0.40 34.41
Engineers, n.e.c. 0.24 7.31
Computer systems analysts and 1.45 3.96
scientists
Teachers, elementary school 0.71 0.06
Teachers, secondary school 0.23 0.02
Electrical and electronic 0.22 4.01
technicians
Supervisors and proprietors, sales 8.19 1.60
occupations
Real estate sales occupations 1.74 0.44
Sales rep., mining, manufacturing, 4.97 1.94
& wholesale

Total 48.39 73.39

 Political
Occupation History Science

Administrators and officials, 1.97 3.12
public administration
Financial managers 0.70 2.01
Managers, marketing, advertising, 1.70 2.03
& public relations
Managers and administrators, n.e.c. 7.34 10.46
Accountants and auditors 3.23 2.18
Other financial officers 1.07 3.31
Aerospace engineers 0.03 0.10
Electrical and electronic engineers 0.18 0.12
Engineers, n.e.c. 0.30 0.03
Computer systems analysts and 0.86 1.16
scientists
Teachers, elementary school 9.68 3.58
Teachers, secondary school 4.32 0.77
Electrical and electronic 0.13 0.06
technicians
Supervisors and proprietors, sales 4.59 3.81
occupations
Real estate sales occupations 0.92 1.31
Sales rep., mining, manufacturing, 1.26 3.88
& wholesale

Total 36.31 34.81

Source: Authors' calculation, NSCG.

Notes: All occupations that employ at least 3% of one of the six majors
is listed. Respondents are full-time workers n.e.c. is not elsewhere
classified.

TABLE 2

1990 Wage Gaps Relative to Economics by College Major of Full-Time
Workers Aged 25 to 55 with a Bachelors Degree, 1993 NSCG

 Wages Relative to
Major Economics Major (%)

Science

Biology -16.23 ***
Chemistry -4.15
Geology -14.99 ***
Math 2.45
Physics -2.20

Engineering and CS

Aerospace engineering 8.88 **
Chemical engineering 21.49 ***
Civil engineering 4.13
Computer science 8.49 ***
Electrical engineering 15.87 ***
Industrial engineering 2.07
Mechanical engineering 10.54 ***
Electric technology 2.62
Mechanical technology 1.71

Education

Elementary -l7.69 ***
Physical -l9.68 ***
Secondary -23.85 ***

Business

Accounting -0.79
Business administration -l0.74 ***
Finance -1.17
Marketing -6.89 ***
Other business -14.04 ***

Other professional degrees

Agricultural & environmental design -17.37 ***
Communication -15.66 ***
Conservation -28.27 ***
Criminology -17.69 ***
Home economics -25.43 ***
Journalism -15.71 ***
Social work -27.81 ***

Health

Medical technology -4.56 *
Nursing 5.24 **

Social science

Economics 0.00
History -18.21 ***
Political science -13.41 ***
Psychology -17.98 ***
Sociology -18.67 ***

Humanities

English -l5.56 ***
Foreign languages -l5.95 ***
Philosophy & theology -47.56 ***

Arts

Fine arts -27.72 ***
Music -37.31 ***


Source: Authors' calculation, NSCG.

Notes: Dependent variable is the natural log of wage in 1990. The
regression nonparametrically controls for race-ethnicity (white, black,
Hispanic, Asian, and Native American), age, and gender. There are 85
different major controls, but only selected ones are reported.
Huber-white standard errors are used to calculate significance levels
with clustering for each cell. There are 55,422 observations used in the
regression. Each reported major has at least 350 observations. Sample
weights are used for these calculations.

* Indicates significance at 0.10 level.

** Indicates significance at 0.05 level.

*** Indicates significance at 0.01 level.

TABLE 3

1990 Wage Gaps Relative to Economics by College Major and Gender or Age
of Full-Time Workers Aged 25 to 55 with a Bachelor's Degree, 1993 NSCG

 Gender Age Category
 Women Men Under 35

Sciences

Biology -12.64 *** -18.69 *** -16.99 ***
Math 8.72 ** -1.06 1.90
Computer science 15.99 *** 5.57 * 12.34 ***

Education
Elementary -16.62 *** -26.96 *** -21.23 ***
Physical -14.41 *** -23.27 *** -23.36 ***
Secondary -20.74 *** -27.85 *** -20.76 ***

Business
Accounting 0.35 -1.40 -0.60
Business administration -9.71 *** -11.09 *** -11.33 ***
Marketing -10.69 *** -5.22 * -9.58 ***
Other business -15.95 *** -13.22 *** -14.42 ***

Social science & humanities
Economics 0 0 0
History -16.74 *** -18.90 *** -23.40 ***
Political science -7.44 * -15.76 *** -16.64 ***
Psychology -19.51 *** -16.58 *** -19.81 ***
Sociology -17.62 *** -20.30 *** -18.40 ***
English -13.21 *** -19.44 *** -16.98 ***
Foreign languages -11.20 *** -28.31 *** -20.71 ***

 Age Category
 35 to 44 45 to 55

Sciences

Biology -14.56 *** -19.62 ***
Math 5.14 -1.83
Computer science 4.32 --

Education
Elementary -18.85 *** -13.16 **
Physical -18.02 *** -17.95 ***
Secondary -25.68 *** -23.96 ***

Business
Accounting -0.48 -2.02
Business administration -11.59 *** -8.57 *
Marketing -3.81 0.74
Other business -12.38 *** -15.50

Social science & humanities
Economics 0 0
History -17.46 *** -14.57 **
Political science -8.86 * -15.14
Psychology -16.64 *** -18.65 ***
Sociology -18.03 *** -21.58 ***
English -20.13 *** -6.14
Foreign languages -14.77 *** -10.56

Source: Author's calculation, NSCG.

Notes: Dependent variable is the natural logarithm of wage in 1990. The
regression nonparametrically controls for race-ethnicity (white, black,
Hispanic, Asian, and Native American), age, and gender. There are 85
different major controls, but only selected ones are reported.
Huber-White standard errors are used to calculate significance levels
with clustering for each cell. There are 55,422 observations used in the
regression. Each reported major cell has at least 100 observations.
Sample weights are used for these calculations.

* Indicates significance at 0.10 level.

** Indicates significance at 0.05 level.

*** Indicates significance at 0.01 level.

TABLE 4

Propensity for Pursuing Advanced Degrees by Undergraduate College Major,
Full-Time Workers Aged 35 to 55, 1993 NSCG

 Bachelor's Professional
 Only Master's Degree

Science
Biology 41.32 25.91 20.69
Chemistry 37.67 22.14 13.04
Maths 50.07 37.70 2.61
Physics 33.53 32.43 2.34

Engineering and computer science
Chemical engineering 51.89 33.64 3.22
Civil engineering 62.83 32.23 0.74
Computer science 78.69 19.67 0.06
Electrical engineering 59.37 32.47 1.55
Mechanical engineering 63.25 29.68 1.92

Education
Elementary 57.22 40.65 0.70
Physical 54.81 41.79 1.41
Secondary 51.15 43.88 2.62

Health
Nursing 71.72 24.46 1.61

Business
Accounting 80.06 16.27 2.52
Business administration 81.74 15.05 2.24
Marketing 80.65 17.63 1.15

Other professional degrees
Social work 60.59 36.60 1.79

Social science
Economics 55.41 29.84 8.95
History 48.85 34.59 11.52
Political science 46.10 24.40 24.68
Psychology 48.43 33.67 7.21
Sociology 59.47 32.12 4.10

Humanities
English 47.37 38.35 7.00
Foreign languages 48.34 37.20 6.07
Philosophy & theology 39.10 40.25 9.81

Arts
Fine arts 68.65 27.24 2.48


 Ph.D.

Science
Biology 12.08
Chemistry 27.15
Maths 9.62
Physics 31.70

Engineering and computer science
Chemical engineering 11.26
Civil engineering 4.20
Computer science 1.57
Electrical engineering 6.61
Mechanical engineering 5.16

Education
Elementary 1.43
Physical 1.99
Secondary 2.36

Health
Nursing 2.21

Business
Accounting 1.16
Business administration 0.96
Marketing 0.57

Other professional degrees
Social work 1.02

Social science
Economics 5.80
History 5.04
Political science 4.82
Psychology 10.70
Sociology 4.32

Humanities
English 7.28
Foreign languages 8.39
Philosophy & theology 10.83

Arts
Fine arts 1.64

Source: Authors' calculation, NSCG.

Notes: There is a minimum of 700 observations for each major. Sample
weights are used for these calculations.

TABLE 5

Highest Degree Obtained for Four Undergraduate Majors (%), Individuals
Aged 25 to 55, 1993 NSCG

 Electrical Political
Highest Degree Economics Engineering History Science

Bachelor's 62.40 66.34 52.79 51.38

Master's 24.67 27.61 30.67 20.35

Distribution of students
obtaining the degree

Own discipline 22.79 53.69 18.57 17.68
Education 6.22 0.48 49.79 19.62
Business 53.04 22.33 12.69 23.81
Public administration 3.78 0.50 3.67 16.01

Professional degree 8.77 1.41 12.42 24.69

Distribution of students
obtaining the degree

Medicine 6.32 29.18 6.93 1.14
Law 88.29 49.34 84.57 96.02

Ph.D. 4.17 4.64 4.12 3.57

Distribution of students
obtaining the degree

Own discipline 61.06 63.71 47.26 62.84
Education 4.67 1.03 21.05 13.94
Business 11.07 1.31 2.13 2.76
Public administration 0.34 0.00 0.93 3.46

Source: Authors' calculation, NSCG.

Notes: Respondents are aged 25 to 55, inclusive. Percentages in bold
give the overall percentage of the given major. The indented percentages
give the percentage of students study in selected field for that
degrees.

TABLE 6

1990 Wages Gaps Relative to Economics by Undergraduate College Major of
Full-Time Workers Aged 25 to 55 with a Master's Degree in Business, 1993
NSCG

 Wages Relative
 to Undergraduate
Undergraduate Major Economics Major (%)

Science
Biology -22.35 ***
Chemistry -16.84 **
Maths -5.37
Physics -20.67 ***

Engineering and CS
Aerospace engineering -3.42
Chemical engineering 8.23
Civil engineering -10.52
Computer science -11.83
Electrical engineering -1.03
Industrial engineering -10.06 *
Mechanical engineering -17.31 ***

Business
Accounting -18.91 ***
Business administration -19.76 ***
Finance -14.52 **
Marketing -17.97 ***
Other business -19.76 *

Social sciences
Economics 0.00
History -29.71 ***
Political science -7.91
Psychology -22.61 **
Sociology -27.59 **

Humanities
English -8.81
Foreign languages -31.20 *

Source: Authors' calculation, NSCG.

Notes: Dependent variable is the natural log of wage in 1990. The
regression nonparametrically controls for race-ethnicity (white, black,
Hispanic, Asian, and Native American), age, and gender. There are 84
different major controls, but only selected ones are reported.
Huber-White standard errors are used to calculate significance levels
with clustering for each cell. There are 4,012 observations used in the
regression. Each reported major has at least 30 observations. Sample
weights are used for these calculations.

* Indicates significance at 0.10 level.

** Indicates significance at 0.05 level.

*** Indicates significance at 0.01 level.

TABLE 7

1990 Wages Gaps Relative to Economics by Undergraduate College Major of
Full-Time Workers Aged 25 to 55 with a Law Degree, 1993 NSCG

 Wages Relative to Undergraduate
Undergraduate Major Economics Major (%)

Other professional degrees
Criminology -20.33 *

Business
Accounting -9.40
Business administration -23.89 **
Finance -8.76

Social sciences
Economics 0.00
History -l6.23 **
Political science -14.77 **
Psychology -17.05
Sociology -35.53 ***

Humanities
English -l6.89 **
Foreign language -2.26
Philosophy & theology -30.l3 **

Source: Authors' calculation, NSCG.

Notes: Dependent variable is the natural log of wage in 1990. The
regression nonparametrically controls for race-ethnicity (white, black,
Hispanic, Asian, and Native American), age, and gender. There are 78
different major controls, but only those with 30 or more observations
are reported. Huber-White standards errors are used to calculate
significance levels with clustering for ecah cell. There are 2,152
observations used in the regression. Sample weights are used for these
calculations.

* Indicates significance at 0.10 level.

** Indicates significance at 0.05 level.

*** Indicates significance at 0.01 level.

(1.) A small number of individuals who were "too old" apparently gave incorrect responses to the age question in the 1990 Census. The very high level of measurement error in education in the 1990 Census (and, by extension, for any other similar surveys, such as the Current Population Survey) poses a more interesting problem. See Black, Sanders, et al. (2002) for detailed analysis.

(2.) Respondents were considered refusals unless they provided information about their last degree and field of study.

(3.) This required combining several small major groups. Details are available from the authors.

(4.) Because the data have extensive demographic information, they are well suited for studying race/ethnic wage differentials among the well educated. See Black, Haviland, et al.(2002).

(5.) Only 0.03% of the earnings in our sample are top coded.

(6.) This would be equivalent to a fixed effect estimator if the covariates were independent of college major.

(7.) One could try similar empirical exercises, focusing on wage variation within specific occupations. We notice, for example, from Table 1 that a substantial fraction of individuals in each major are "managers and administrators." To satisfy curiosity we estimated the regression that forms the basis of Table 2 (using college graduates with no advanced degrees) but for the 3,055 individuals in this occupational category. We estimate coefficients as follows: -14.60 for history (not significant), -14.15 for business administration (significant at 0.05). -21.62 for political science (not significant), -3.84 for accounting (not significant), and -0.19 for electrical engineering (not significant). Point estimates are reasonably similar to those reported for the entire sample in Table 2 because the unweighted correlation between the two sets of coefficients is 0.50.

(8.) Because we do not "clean" our wage variable (see Bollinger and Chandra [2001] for strong arguments for not "cleaning" data) and our earnings data is top coded at $999,999, we were concerned that our results might be sensitive to outliers. We reestimated our equation using median regression, however, and our results were essentially unchanged. The unweighted correlation coefficient between the ordinary least squares and median regression coefficients was 0.96.

(9.) The sets of coefficients for men and women are quite similar, with an unweighted correlation coefficient of 0.70.

(10.) Again, the sets of coefficients are reasonably stable across cohorts. The unweighted correlation coefficient between the coefficients for the under-35 and 35-to-44 cohorts is 0.80. The unweighted correlation coefficient between the 35-to-44 and the 45-to-55 cohorts is 0.54 and for the under-35 and the 45-to-55 cohorts is 0.60.

(11.) Our approach is similar to the approach of Heckman et al. (1997) and Black, Smith, et al. (2002) using experimental data.

(12.) The same holds true for the other majors. For political science the median coefficient is 15.1%, compared to the OLS coefficient of 13.4; for history the median coefficient is 17.5%, compared to the OLS coefficient of 18.2; and for business administration the median coefficient is 10.5% compared to the OLS coefficient of 10.7.

(13.) When respondents have both a professional degree (e.g., a J.D. or M.D.) and a Ph.D., we assume that the Ph.D. is the more advanced degree.

(14.) See the College Board, www.collegeboard.org/sat/cbsenior/yr200l/pdf/NATL.pdf, accessed on 30 December 2001. The College Board provides only very rough major grouping (e.g., it aggregates all social sciences). It is interesting to note that the total SAT score (M + V) among seniors planning to enter the most poorly compensated major, philosophy/religion/theology (539/561), is slightly higher than for those planning to enter the best compensated major, Engineering (572/523).

REFERENCES

Black, D., S. Sanders, and L. Taylor. "Measurement of Higher Education in the Census and CPS." Unpublished paper, Carnegie Mellon University, 2002.

Black, D., A. Haviland, S. Sanders, and L. Taylor. "Why Do Minority Men Earn Less? A Study of Wage Differentials among the Highly Educated." Unpublished paper, Carnegie Mellon University, 2002.

Black, D., J. Smith, M. Berger, and B. Noel. "Is the Threat of Reemployment Services More Effective than the Services Themselves? Experimental Evidence from UI Claimant Profiling." National Bureau of Economic Research Working Paper No. 8825, 2002.

Bollinger, C., and A. Chandra. "Jatrogenic Specification Error: Cleaning Data Can Exacerbate Measurement Error Bias." Unpublished paper, Dartmouth College, 2001.

College Board. "2001 College Seniors: A Profile of SAT Program Test Takers." Unpublished paper, College Entrance Board, Princeton, NJ, 2001.

Heckman, J. J., J. A. Smith, and N. Clements. "Making the Most out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts." Review of Economic Studies, 64(4), 1997, 487-535.

RELATED ARTICLE: ABBREVIATIONS

NSCG: National Survey of College Graduates

NSF: National Science Foundation

OLS: Ordinary Least Squares

DAN A. BLACK, SETH SANDERS, and LOWELL TAYLOR *

* We thank Alan Krueger for comments on an earlier version of the paper.

Black: Professor, Center for Policy Research, Syracuse University, Syracuse, NY 13244. Phone 1-315-443-9040, Fax 1-315-443-1081, E-mail [email protected]

Sanders. Professor, Department of Economies, University of Maryland, College Park, MD 20742-7211. Phone 1-301-405-3497, Fax 1-301-405-3542, E-mail [email protected]

Taylor: Professor, Department of Economics and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213-3890. Phone 1-412-268-3278, E-mail [email protected]