Do undergraduate majors or Ph.D. students affect faculty size?
Becker, William E. ; Greene, William H. ; Siegfried, John J. 等
Most academic economists at one time or another have participated
in department meetings in which the relationship between the number of
students handled by the department and the number of faculty positions
in the department has been discussed. They have watched department
chairs invariably parade recently rising numbers in economics courses
before their deans when requesting additional faculty slots (while often
remaining mute when the numbers decline). Some faculty are cynical about
the probable administrative response, anticipating that deans are likely
to allow class sizes to rise during periods of increasing student
demand, especially for short periods, because the expansion of tenured
or tenure-track faculty is difficult to reverse if students numbers
subsequently should decline.
Isaac Ehrlich (2006), Department of Economics Chair, University of
Buffalo, however, provides evidence that, at least in his administrative
experience, faculty size really has been driven by students. He observed
that in 2000 his department had sunk to 10 full-time tenured and
tenure-track members, down from 18 in 1991. "Since the 1997
academic year, however, the department has experienced a
multidimensional revival. Faculty size is back to 18 this fall ... We
also have experienced a tremendous growth in the number of students we
serve, primarily at the graduate level, which also serves as the engine
of faculty growth." Similarly, but in the opposite direction, we
have the recent occurrence at Southern Mississippi University where a
low number of economics majors (average of five per year) has led to an
administrative decision to shrink the economics faculty at Southern
Mississippi University from nine to five, resulting in four involuntary
"early retirements." (Celano 2009). The Southern Mississippi
administration first proposed to eliminate the department completely,
but salvaged five positions to service other departments that require
economics courses in their majors, leaving a reduction of four due just
to the low number of majors.
Consistent with this anecdotal evidence, Johnson and Turner (2009),
using the canonical model of dynamic labor demand in Sargent (1978),
calculate an elasticity of faculty with respect to student demand to be
0.04 in the short-run and 0.6 in the long-run. These generic
elasticities, however, tell us little about the response of faculty
lines to changing numbers of degrees awarded or whether it is
undergraduate or graduate degrees that drive faculty size in departments
that offer both degrees. Johnson and Turner (2009) do propose that some
university administrators/managers may view research quality and
graduate training as substitutes for providing more course options or
smaller classes for undergraduates. They conclude based on their
individual institution statistics that those departments that are rated
higher on research quality are less likely to "shoulder the
heaviest burdens of undergraduate teaching and advising (p. 182). They
also state, however, that a substantial part of the explanation for
differences in student-faculty ratios across academic departments
"may reside in the politics (traditional policy) rather than the
economics of decision making in institutions of higher education,"
(p. 170) because in a pure economic model, student demand determines
faculty allocations. In a political economy model, political power
determines the allocation of resources and rents. Highly vocal faculty
members engaging in persistent lobbying may limit the extent to which
administrators can adjust faculty lines to better match student demand
without paying a high personal cost.
The responsibilities of a typical economics department include a
variety of tasks that extend beyond providing for the education of
undergraduate majors and Ph.D. students: general education (principles
of economics and seminars for first-year students), service courses for
other departments (e.g., money and banking for business majors),
interdisciplinary teaching, occasionally a master's program,
faculty research and publication, and faculty service (e.g., media
relations, extension and other outreach activities, especially at public
universities). Changes in the demand for any of these services can at
least in theory create incentives for a supply response. The critical
issue, however, comes back to the relationship between faculty size and
students if changes in student demand drive the employment of faculty.
While enrollment by students satisfying general education requirements
and those majoring in other disciplines contribute to student demand, it
is the number of undergraduate majors and Ph.D. students that usually
attracts the most attention among various measures of a
department's teaching responsibilities, primarily because these
measures are easiest to count.
Here we examine whether undergraduate degrees (BA and BS) in
economics or Ph.D. degrees in economics drive the tenured and
tenure-track faculty size at those institutions that offer only a
bachelor's degree and those that offer both bachelor's degrees
and Ph.D.s. (1) At bachelor's degree level institutions, the number
of permanent faculty primarily is determined by a short-term moving
average and a long-term average number of students, with annual
deviations from the long-run mean having little effect on tenured and
tenure-track faculty size in departments of economics. Adjustments in
instructional resources, if they are made in response to short-run
volatility, must take the form of adding or subtracting term-appointment
lecturers and adjunct professors. In a similar fashion, at institutions
awarding both the bachelor's degree and Ph.D., the number of
tenured and tenure-track faculty is predicted to depend on the long-term
target number of Ph.D.s to be awarded per year and not on either annual
deviations from this long-term average, or on the average level of or
short-run variation in the number of undergraduate economics students.
I. Data
Our sample observations come mostly from data collected annually by
the American Economic Association (AEA). The number of undergraduate
economics degrees per institution per year is taken from the AEA's
Universal Academic Questionnaire (UAQ), supplemented by e-mail requests
to individual departments. These data form the basis for a report that
has been published by one of us annually for many years in the Summer
issue of the Journal of Economic Education (Siegfried 2008). The numbers
of Ph.D. degrees in economics awarded by departments are obtained from
the Survey of Earned Doctorates, which is jointly sponsored by a
half-dozen federal government agencies. So far as we know, student
enrollment data are not available by department by institution.
We have degree data for each year from 1990-91 through 2005-06 for
every included institution, with one exception: data on Ph.D. degrees
were not collected for 1998-99. We measure degrees rather than majors or
number of enrolled Ph.D. students because undergraduate students declare
their major at different points during their educational experience at
different colleges and universities, and Ph.D. enrollments do not
correlate well with either students doing coursework, students on
campus, or completions. The sample period begins in 1990-91 because that
is the year that was selected as a benchmark for a study of the
precipitous decline in undergraduate economics majors that occurred in
the mid-1990s. The period ends with 2005-06 because those were the
latest data available when we began the present study. Fortunately
1990-91 through 2005-06 includes a complete cycle of undergraduate
degrees, the aggregate numbers declining by over 30 percent of initial
year values in the mid-1990s, and then more than fully recovering over
the subsequent decade.
The number of full-time tenured or tenure-track faculty also are
collected from the UAQ. We included in our sample each institution for
which we also have undergraduate economics degree data and for which the
number of years of missing faculty data is no more than three over the
entire 16 year interval for each institution, with no two consecutive
years missing for any institution. We are missing three percent of
faculty observations for the Ph.D. institutions, and six percent for the
bachelor's institutions. Rather than employing a multiple random
imputation procedure to handle the missing observations, we interpolated
missing data on the number of faculty from the reported information in
the years prior and after a missing observation. Due to the nature of
faculty hiring (a slow, annual process), the missing observation is
often the same as both the number of faculty in the year prior and the
year after the missing observation. (2) In a few cases, the department
provided a precise number from its records to replace a missing
observation.
The result is a sample of 16 years of data for each of 18 colleges
for which the bachelor's degree is the highest degree awarded in
economics, and 24 universities for which a Ph.D. is the highest degree
awarded in economics (see Appendix for names). The 18 colleges for which
the bachelor's degree is the highest degree awarded all emphasize
teaching. In terms of the objectives and constraints of the different
types of institutions, we would expect the strongest response of
permanent faculty numbers to degrees to occur at such teaching oriented
colleges, where class size is an important characteristic that
distinguishes them from research universities. We would expect the
weakest response of permanent faculty to the number of undergraduate
degrees at universities that offer a Ph.D. in economics because the
missions those institutions embrace, possibly even emphasize, are
graduate education and faculty research. Undergraduate education, and
especially class size, is a less important concern at research
universities.
Table 1 provides descriptive statistics on the 18 bachelor's
degree granting colleges and the 24 universities offering both
bachelor's and doctorate degrees in the 16 years from 1991 through
2006. The number of Ph.D.s awarded in 1999 is not available from the
Survey of Earned Doctorates (or anywhere else). To sustain the balanced
panels for the entire period, for 1999 we inserted the mean of the 1998
and 2000 numbers of Ph.D.s awarded by each of the 24 universities. Not
surprisingly, both the distribution of bachelor's and Ph.D. degrees
granted and number of full-time tenured or tenure-track faculty members
are positively skewed. One bachelor's degree granting institution
awarded no degrees in 1995, which likely would have spelled the end of
the department had it not soon thereafter restored a positive number of
graduates. One Ph.D. granting private university awarded no Ph.D.
degrees and only four bachelor's degrees in 1992 but these were
aberrations compared to its long-ran average of two and seven respective
degrees per year. At the other extreme, in 2003 a maximum of 45 Ph.D.
degrees (and 409 bachelor's degrees) were awarded by one large
state university that averaged 32 PhD. degrees (and 394 bachelor's
degrees) over the 1991-2006 period. The largest number of economics
bachelor's degrees, 682, was awarded in 2003 by a public university
that awarded 9 Ph.D. degrees that year, and averaged 553 bachelor's
degrees and 6 Ph.D. degrees over the entire period.
Private institutions (PRIVATE = 2) are more prevalent than public
institutions (PUBLIC = 1) in the sample for both bachelor's and
Ph.D.-granting institutions; this is especially so for the
bachelor's level. Finally, a binary variable that indicates the
absence or presence of a business degree program is included based on
findings reported in the series of empirical studies addressing the
effect of a competing business program on the number of undergraduate
economics majors that appeared in the Fall 1996 issue of the Journal of
Economic Education, (Salemi 1996). Those studies find that fluctuations
in excess demand for competing business degree programs affect economics
department enrollments. By including an indicator of competing business
programs, we test whether fluctuations in economics majors caused by
changes in the business programs have a differential effect on faculty
positions vis-a-vis the number of economics majors generated otherwise.
For the undergraduate programs this 0-1 dummy variable (Bprog) simply
reflects whether there is a business program. For institutions with a
Ph.D. program in economics, an analogous MBA dummy variable was created
to test whether the instructional servicing of MBAs influences faculty
size.
II. Basic Model and Estimates
As a starting point, consider the pooled least squares estimates of
the models of permanent faculty size for the two classes of institutions
in Table 1. We assume the faculty-size-generating process for
bachelor's degree-granting undergraduate departments is:
FACULTY [size.sub.it] = [[beta].sub.1] + [[beta].sub.2][YEAR.sub.t]
+ [[beta].sub.3] [BA&S.sub.it] + [[beta].sub.4][MEANBA&S.sub.i]
+ [[beta].sub.5][MOVAVBA&BS.sub.it] + [[lambda].sub.6][PUBLIC.sub.i]
+ [[beta].sub.7][Bprog.sub.i] + [[epsilon].sub.it] + [u.sub.i]
where error term [[epsilon].sub.it] is iid across institutions and
over time and E([[epsilon].sub.it.sup.2]|[x.sub.it]) = [[sigma].sup.2],
for n = 18 schools and T = 14 years, and for PhD and bachelor's
degreegranting departments is:
FACULTY [size.sub.it] = [[lambda].sub.1] +
[[lambda].sub.2][YEAR.sub.t] + [[lambda].sub.3] BA&[S.sub.it] +
[[lambda].sub.4]MEANBA&[S.sub.i] +
[[BETA].sub.5]MOVAVBA&B[S.sub.it] + [[lambda].sub.6][PHD.sub.it] +
[[lambda].sub.7][MEANPHD.sub.i] + [[lambda].sub.8][MOVAVPHD.sub.i] + +
[[lambda].sub.9][PUBLIC.sub.i] + [[lambda].sub.10][MBA.sub.i] +
[[epsilon].sub.it]
where error term [[epsilon].sub.it] is iid across institutions and
over time and E([[epsilon].sub.it.sup.2]|[x.sub.it]) = [[sigma].sup.2],
for n = 24 schools and T = 14.
There are three ways in which we entertain the effect of degrees on
faculty size. First, an implied justification for including the number
of contemporaneous degrees ([BA&S.sub.it], [PHD.sub.it]) is that the
decision makers might form a type of rational expectation in that they
set the permanent faculty size based on the anticipated number of majors
to receive degrees in the future. Second, we have included the overall
mean number of degrees awarded at each institution
([MEANBA&S.sub.i], [MEANPHD.sub.i]) to reflect a type of historical
steady state. That is, the central administration or managers of the
institution may have a target number of permanent faculty relative to
the long-term expected number of annual graduates from the department
that is desired to maintain the department's appropriate role
within the institution. (3) Third, the central authority might be
willing to marginally increase or decrease the permanent faculty size
based on the near term trend in majors, as reflected in a three year
moving average of degrees awarded ([MOVAVBA&BS.sub.it],
[MOVAVPHD.sub.i]).
The OLS estimates for bachelor's granting colleges, with
standard errors adjusted for each college's potential unique random
component, are reported in Table 2, Panel A. The marginal effect of an
additional economics major is insignificant and even slightly negative
within the sample. However, if a department of economics can document an
upward trend in degrees (as reflected in the three-year moving average),
then the college will respond with additional tenure-track lines. It
takes an increase of 26 or 27 bachelor degrees in the moving average to
expect just one more faculty position. Tenured and tenure-track faculty
size is largely and significantly determined by the institution's
desired student numbers (as represented by average number of
bachelor's degrees). A long-term increase of nine or ten students
earning degrees in economics is required to predict one more faculty
member is in a department.
Moving from a public to a private institution lowers predicted
faculty size by nearly four members, ceteris paribus and on average
increases the ratio of annual graduates to faculty from 3.6 to 9.0, an
enormous difference. There is an insignificant erosion of tenured and
tenure-track faculty size over time. Finally, while economics
departments in colleges with a competing business program tend to have a
larger permanent faculty, ceteris paribus, the effect is small and
insignificant.
At a university with a Ph.D. program in economics (Table 2, Panel
B), the marginal effect of an additional undergraduate economics major
or change in short or long term undergraduate degree average is
statistically insignificant (standard errors adjusted for clustering).
The size of the bachelor's program does not appear to matter.
Rather, it is the average size of the Ph.D. program that drives faculty
size at research universities. Little more than one additional Ph.D.
student added to the long-term average Ph.D. class size is required in
order for predicted faculty size to increase by one, ceteris paribus.
Based on the lack of significance in the three-year Ph.D. degree moving
average and small but significant effect of contemporaneous Ph.D.
degrees, changing faculty size at Ph.D. granting institutions appears to
be a daunting challenge.
There seems to be no secular decline in full-time permanent faculty
numbers at Ph.D. granting universities or any difference between typical
permanent faculty size at public and private research universities, In
addition, the presence of an MBA program is innocuous.
III. Random Effects Models and Estimates
There are likely to be substantial school specific effects in the
proposed regression models. A natural approach to take in this case is
to add "fixed school effects" to the regression by adding
institution specific dummy variables to the model. In our case (as often
happens in analyzing microeconomic level data) the fixed effects
approach is unworkable because other time invariant variables in the
model (e.g., PUBLIC in both equations) will be collinear with the set of
school dummy variables. The alternative approach to incorporating school
specific effects is a random effects model. However, the random effects
model makes the strong assumption that the random school effects are not
correlated with the other explanatory variables in the model.
Mundlak's (1978) approach to modeling panel data is a commonly used
specification that seeks a middle ground between these two formulations.
The Mundlak model posits that the fixed effect in the equation,
[[alpha].sub.i], can be projected upon the group means of the time
varying variables, so that
[[alpha].sub.i] - [[beta].sub.1] +
[[delta].sup.'][[bar.x].sub.i] + [u.sub.i]
where [[bar.x].sub.i] is the set of group (school) means of the
time varying variables and [u.sub.i] is a (now) random effect that is
uncorrelated with the variables and disturbances in the model.
Logically, adding the means to the equations picks up the correlation
between the school effects and the other variables. Adding the means of
the numbers of degrees awarded, as we have already done in the two
equations, has the added benefit of enabling us to follow the Mundlak
approach to panel data modeling and estimation.
We have completed the model by formulating the random effects
models for BA and BS degree-granting undergraduate departments as:
FACULTY [size.sub.it] = [[beta].sub.1] + [[beta].sub.2][YEAR.sub.t]
+ [[beta].sub.3] [BA&S.sub.it] + [[beta].sub.4][MEANBA&S.sub.i]
+ [[beta].sub.5][MOVAVBA&BS.sub.it] + [[lambda].sub.6][PUBLIC.sub.i]
+ [[beta].sub.7][Bprog.sub.i] + [[epsilon].sub.it] + [u.sub.i]
where error term [epsilon] is iid over time and
E([[epsilon].sub.it.sup.2]|[x.sub.it] = [[sigma].sup.2] for n = 18 and
[T.sub.i] = 14 and E[[u.sib.i.sup.2]] = [[theta].sup.2] for n = 18: and
for PhD and bachelor degree-granting departments as:
FACULTY [size.sub.it] = [[lambda].sub.1] +
[[lambda].sub.3][BA&S.sub.it] + [[lambda].sub.4][MEANBA&S.sub.i]
+ [[beta].sub.5][MOVAVBA&BS.sub.it][[lambda].sub.6][PHD.sub.it] +
[[lambda].sub.7][MEANPHD.sub.i] + [[lambda].sub.8][MOVAVPHD.sub.i] + +
[lambda].sub.9][PUBLIC.sub.i] + [[lambda].sub.10][MBA.sub.i] +
[[epsilon].sub.it] + [u.sib.i]
where error term [[epsilon].sup.it.sub.2] is iid over time with
E([[epsilon].sup.it.sub.2]|[x.sub.it]) = [[sigma].sup.2] for n = 24 and
T = 14.
The random effects estimates are reported in Table 3. Panel A
contains the estimates for those institutions that award only
bachelor's degrees in economics. The marginal effect of an
additional economics major is again insignificant but slightly negative
within the sample. Both the short-term moving average and long term
average number of bachelor's degrees are significant. A long-term
increase of about 10 students earning degrees in economics is required
to predict that one more tenured or tenure-track faculty member is in a
department. Ceteris paribus, economics departments at private
institutions are smaller than comparable departments at public schools
by a large and significant four members. Whether there is a competing
undergraduate business program present is insignificant. There is no
meaningful trend in faculty size.
Panel B of Table 3 reports the random effects estimates for
universities with both undergraduate and Ph.D. programs in economics. As
with the OLS estimates, it is the long-term average size of the Ph.D.
program that drives permanent faculty size. Little more than a single
Ph.D. student added to the long-term average is required for the
predicted tenured or tenure-track number of faculty to increase by one,
ceteris paribus. In the short run, increasing the number of Ph.D.
degrees in any given year or as a moving average, however, has little,
if any effect. Curiously, the marginal effect of a short term moving
average increase in undergraduate economics major is statistically
significant at the 0.10 Type I error level, but the effect remains
small. There is no statistical significance and little effect associated
with trend, public versus private or whether the university has an MBA
program.
IV. Conclusion
Random effects estimates to predict the number of economics faculty
at bachelor's degree level colleges suggest that deans primarily
target faculty size to accommodate a specific long-term expected number
of students, adding one faculty member for each additional 10 graduating
majors. Presidents and deans are quite cautious about responding to
short-term deviations from the long-term average. Given the outcry that
can be expected from faculty who are to have their oxen gored for the
possible short-term gain of those with increased student demand, these
central managers have little or no incentive to change the allocation of
resources and rents. (This political power argument obviously depends on
those with the increased student demand being too busy to squeal as loud
as those with time on their hands.)
The magnitudes are quite different at research universities that
produce both bachelor's and Ph.D. degrees. Faculty size at Ph.D.
granting institutions is predicted to increase on a one-for-one basis as
the target number of Ph.D.s awarded per year rises. Although the type of
students (undergraduate versus graduate) driving decisions about
permanent faculty size differs between bachelor's and Ph.D.
granting institutions, in both cases the evidence indicates that it
takes a much larger short-term change in student demand to induce a
change in the number of full-time tenured or tenure-track faculty than
it takes from a long-term change in student demand. These results are
consistent with Johnson and Turner's (2009) conclusion that
student-faculty ratios are driven by tradition that is based more on
past politics than economics.
Appendix
Institutions in the Bachelor's Degree Sample (n = 18)
Amherst College
Bowdoin College
Gonzaga University
Ithaca College
Randolph-Macon Women's College
University of Vermont
Augustana College
Davidson College
Hartwick College
Metropolitan State College
Saint Lawrence College
Ursinus College
Bates College
Eastern Kentucky University
Idaho State University
Texas Lutheran University
University of Richmond
Whittier College
Institutions in Ph.D. Degree Sample (n = 24)
Boston College
California Institute of Technology
Florida State University
Johns Hopkins University
Michigan State University
Purdue University
Southern Methodist University
University of California-Santa Barbara
University of Kansas
University of North Carolina-Chapel Hill
University of Rochester
Washington State University
Brown University
Clark University
Indiana University
Kansas State University
Princeton University
Southern Illinois University-Carbondale
University of California-Berkeley
University of Iowa
University of Nebraska-Lincoln
University of Oregon
University of Wisconsin-Madison
Washington University-St. Louis
References
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University of Buffalo Reporter, 38(1), August 31, 2006.
http://www.buffalo.edu/reporter/vo138/vol 38n1/columns/qa.html?print=1
(last accessed on 5/29/2008)
Celano, Lee. (2009). "When Tenured Professors Are Laid Off,
What Recourse?" Chronicle of Higher Education. (September 28).
Greene, William H. (2008). Econometric Analysis, 6th Ed. Englewood
Cliffs, NJ, Prentice Hall.
Mundlak, Yair (1978). "On the Pooling of Time Series and Cross
Section Data," Econometrica. Vol. 46. No. I (January): 69-85.
Johnson, William R. and Sarah Turner. (2009). "Faculty Without
Students: Resource Allocation in Higher Education," Journal of
Economic Perspectives. Vol. 23. No. 2 (Spring): 169-190.
Salemi, Michael (1996). "Where Have All the Majors Gone?"
Journal of Economic Education. Vol. 27. No. 4 (Fall): 323-325.
Sargent, Thomas J. (1978). "Estimation of Dynamic Labor Demand
Schedules under Rational Expectations." Journal of Political
Economy. Vol. 86, No. 6 (December): 1009-44.
Siegfried, John. (2008). "Trends in Undergraduate Economic
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Notes
(1.) Our specification can only evaluate the association between
faculty size and numbers of students. It is possible that faculty size
drives enrollment. A department with more faculty ceteris paribus, could
offer a more diverse set of course options and/or smaller class sizes,
which could attract more students to the department. We doubt that
prospective undergraduate majors know much about either class sizes
(except in the extreme) or course option possibilities in economics.
Ph.D. students, on the other hand, are likely to know about applied
field possibilities, but Ph.D. admissions slots and/or financial support
opportunities are usually exogenously controlled by the Graduate School.
(2.) Not filling in these few missing values would ender the panel
data analysis impossible. Moreover, any values within the range of the
adjoining values are unlikely to have a substantive effect on regression
coefficient estimates and their standard errors. That is, imputing 8
faculty members in a year for which this value is unknown when the
adjoining years show 7 and 9 faculty members is not going to materially
affect estimates where we have hundreds of observations. A multiple
imputation routine, on the other hand, might enter an unreasonable value
as a candidate for the missing item as an outcome of the random sampling
mechanism. For the example, while it seems almost certain that the
missing datum would be 7, 8 or 9, a multiple imputation algorithm would
not use this information. Indeed, some missing values might be filled
with values outside the range of their neighbors, which is difficult if
not impossible to justify when simply looking at the data. For example,
we could not justify inserting say 4.75 faculty members generated by an
imputation equation for a missing value between a previous year with 7
members and the following year with 9 members. Thus, our simple
interpolation appeared to us to be the most appropriate approach given
the nature of data.
(3.) One of us, as a member on an external review team for a well
known economics department, was told by a high ranking administrator
that the department had received all the additional lines it was going
to get because it now had too many majors for the good of the
institution. Historically, the institution was known for turning out
engineers and the economics department was attracting too many students
away from engineering. This personal experience is consistent with
Johnson and Turner's (2009, p. 170) assessment that a substantial
part of the explanation for differences in student-faculty ratios across
academic departments resides in politics or tradition rather than
economic decision making in many institutions of higher education.
by William E. Becker, William H. Greene and John J. Siegfried *
* William Becker is Professor Emeritus of Economics, Indiana
University, Adjunct Professor of Commerce, University of South
Australia, Research Fellow, Institute for the Study of Labor (IZA) and
Fellow, Center for Economic Studies and Institute for Economic Research
(CESifo). William Greene is Professor of Economics, Stem School of
Business, New York University, Distinguished Adjunct Professor at
American University and External Affiliate of the Health Econometrics
and Data Group at York University. John Siegfried is Professor Emeritus
of Economics, Vanderbilt University, Senior Research Fellow, University
of Adelaide, South Australia, and Secretary-Treasurer of the American
Economic Association. Their e-mail addresses are
<
[email protected]>, <
[email protected]> and
<
[email protected]>. The authors express appreciation
for suggestions provided by participants at a session of the Allied
Social Science Association (San Francisco) and workshop/seminar
presentations at CESifo (Munich, Germany), IZA (Bonn, Germany) and the
Erasmus School of Economics (Rotterdam, The Netherlands).
TABLE 1.
Descriptive Statistics for Departments of Economics in Sample
Departments of Economics (1991-2006)
Bachelor Degree Ph.D. Granting
Granting
Faculty BA/BS Faculty BA/BS Ph.D.
Degrees Degrees Degrees
Mean 6.61 23.78 23.20 119.92 9.58
Standard Dev. 3.21 19.65 10.44 126.22 7.89
Minimum 2 0 8 2 0
Maximum 14 81 56 682 45
Number of Schools Number of Schools
Total 18 24
Private 4 15
Public 14 9
With Competing MBA Program Present
Business Program
7 3
TABLE 2.
Least Squares Regressions for Faculty Members in Economics
Department
Panel A: Bachelor Degree Granting Institutions
Dependent Variable: Faculty
R Squared 0.6484
F 75.29
P (F > 75.29) 0.0000
Observations 252
Coefficient Standard Error *
Intercept 10.1397 0.9106
Year -0.0281 0.0223
BA/BS Degrees -0.0264 0.0187
Mean BA/BS Degrees 0.1083 0.0338
Public -3.8624 0.5695
Business Program 0.5811 0.9425
Moving Avg. BA/BS Degrees 0.0378 0.0280
Panel B: Ph.D. Granting Institutions
Dependent Variable: Faculty
R Squared 0.5777
F 49.56
P (F > 64.782) 0.0000
Observations 336
Coefficient Standard Error *
Intercept 10.5474 5.7106
Year -0.0253 0.0747
Ph.D. Degrees 0.1157 0.0650
BA and BS Degrees 0.0141 0.0202
Public 0.9493 3.4229
MBA Program -0.9735 2.8452
Ph.D. Degree Means 0.7615 0.2797
BA/BS Degree Means -0.0075 0.0127
Moving Avg. Ph.D. Degrees 0.0181 0.1451
Moving Avg. BA/BS Degrees 0.0169 0.0175
Panel A: Bachelor Degree Granting Institutions
P([absolute value
t Statistic of t] > t Stat)
Intercept 11.13 0.0000
Year -1.26 0.2083
BA/BS Degrees -0.99 0.3814
Mean BA/BS Degrees 3.21 0.0015
Public -6.78 0.0000
Business Program 0.62 0.5382
Moving Avg. BA/BS Degrees 2.09 0.0377
* Clustering corrected for 14 observations per institution
P([absolute value
t Statistic of t] > t Stat)
Intercept 1.85 0.0657
Year -0.34 0.7354
Ph.D. Degrees 1.78 0.0761
BA and BS Degrees 0.70 0.4867
Public 0.28 0.7817
MBA Program -0.34 0.7324
Ph.D. Degree Means 2.73 0.0068
BA/BS Degree Means -0.59 0.5557
Moving Avg. Ph.D. Degrees 0.13 0.9007
Moving Avg. BA/BS Degrees 0.97 0.3353
* Clustering corrected for 14 observations per institution
TABLE 3.
Random Effects Regressions for Faculty Members in Economics
Department
Panel A: Bachelor Degree Granting Institutions
Dependent Variable: Faculty
R Squared 0.6483 (Based on feasible GLS
residuals)
Institution Specific Variance 0.6431; Common Variance ([u.sub.i]):
([[epsilon].sub.it]): 2.9015; Correlation: 0.8186
Observations 18 Institutions, 14 Years
Coefficient Standard Error *
Intercept 10.1419 0.8746
Year -0.0285 0.0215
BA/BS Degrees -0.0161 0.0179
Mean BA/BS Degrees 0.1061 0.0323
Public -3.8637 0.5469
Business Program 0.5818 0.9050
Moving Avg. BA/BS Degrees 0.0398 0.0173
Panel B: Ph.D. Granting Institutions
Dependent Variable: Faculty
R Squared 0.5758 (Based on feasible GLS
residuals)
Institution Specific Variance 5.9694; Common Variance ([u.sub.i]):
([[epsilon].sub.it]): 40.7372; Correlation: 0.8722
Observations 24 Institutions, 14 years
Coefficient Standard Error *
Intercept 10.5780 5.5242
Year -0.0268 0.0729
Ph.D. Degrees 0.0181 0.0641
BA/BS Degrees 0.0051 0.0182
Public 0.9467 3.3169
MBA Program -1.0024 2.7770
Ph.D. Degree Means 0.9052 0.2813
BA/BS Degree Means -0.0113 0.0120
Moving Avg. Ph.D. Degrees -0.0264 0.1400
Moving Avg. BA/BS Degrees 0.0295 0.0159
Panel A: Bachelor Degree Granting Institutions
P([absolute value
t Statistic of t] > t Stat)
Intercept 11.60 0.0000
Year -1.33 0.1838
BA/BS Degrees -0.90 0.3696
Mean BA/BS Degrees 3.29 0.0010
Public -7.07 0.0000
Business Program 0.64 0.5203
Moving Avg. BA/BS Degrees 2.31 0.0212
* Clustering corrected for 14 observations per institution
Panel B: Ph.D. Granting Institutions
P([absolute value
t Statistic of t] > t Stat)
Intercept 1.92 0.0555
Year -0.40 0.6911
Ph.D. Degrees 0.28 0.7783
BA/BS Degrees 0.28 0.7802
Public 0.29 0.7753
MBA Program -0.36 0.7181
Ph.D. Degree Means 3.22 0.0013
BA/BS Degree Means -0.95 0.3340
Moving Avg. Ph.D. Degrees -0.19 0.8503
Moving Avg. BA/BS Degrees 1.87 0.0622
* Clustering corrected for 14 observations per institution
