53 F.G. Mixon Jr, R.W. McKenzie Economics of Education Review 18 1999 51–58
related to CEO compensation in a way consistent with DeAlessi’s analysis. The present paper explores the ten-
ure of managers in the higher education industry in the United States in order to assess the validity of the theor-
etical construct of DeAlessi, Crain and Zardkoohi, and others. While other studies see Toma, 1986, 1990 have
discussed agency problems related to boards of trustees within the operation of higher education, no study to date
has examined the influence of organizational ownership on managerial executive tenure in this industry.
3. The model and data
As a test of our hypothesis, we propose the following statistical model developed with a sample of U.S. univer-
sities and colleges: Tenure 5 a
1 b
1
Public 1 b
2
Urban 1 b
3
PhD 1
1 b
4
Roll 1 b
5
AR 1 b
6
FA 1 b
7
ACT 1 e
i
The dependent variable above, Tenure, is equal to the current year of our data set 1994 minus the year in
which the universitycollege in the sample was founded the number of years the institution has existed, divided
by the number of presidents managers that the UniversityCollege has had in its history. This variable
represents the average tenure number of years for presi- dents across institutions, and is not necessarily normally
distributed. Therefore, various OLS models will be com- pared with maximum likelihood estimates. The foun-
dation date for each school is provided by America’s Best Colleges
from U.S. News and World Report, 1994. Data
on the
number of
presidents for
each universitycollege in the sample comes from microfiche
forms of each universitycollege student bulletin for 1994. This information was usually found in the section
titled “Historical Background” for each institution’s bull- etin. Our sample contains 73 institutions of higher edu-
cation, and summary statistics for our dependent variable Tenure and the other variables are found below in
Table 1.
2
Among the independent variables is Public, a binary
2
Our data set was limited by the availability of the microfiche forms containing information on the “Historical
Background” for each institution. Among the thousands of insti- tutions of higher education, we had approximately 80
microfiche bulletins available to us. Of these, 7 did not contain the appropriate information. A more expansive and informative
data set would be desirable by allowing us to 1 include a much larger number of institutions, and 2 capture the richness
of the institutions’ history and avoid using end-year 1994 data see
5
below. This would constitute a large panel data set which currently is not available.
Table 1 Summary statistics
Variable Mean
Standard deviation Tenure
11.51 4.02
Public 0.44
0.50 Urban
0.51 0.50
PhD 0.34
0.48 Roll
4424.71 5102.86
AR 7998.27
5565.99 FA
63.86 19.35
ACT 21.88
2.65
variable that takes the value of one for public insti- tutions, and zero for private institutions. As a test of the
theory presented above, one expects that the coefficient for Public will be positive and significant, as executive
leaders of the public firms institutionalize decision- making in a way that enhances their utility. As discussed
above, one non-pecuniary source of utility is represented by tenurejob security. One potential problem here is the
possibility
that private non-profit organizations and priv- ate for-profit organizations have different incentive struc-
tures. Recent research indicates Mobley, 1997 that private
non-profit hospitals
attempt to
achieve efficiencies through multi-hospital acquisitions to the
same degree as for-profit hospitals. Peters 1993 also reports efficiency gains of non-profit firms over their
for-profit counterparts in certain regulated industries, and he states that the current property rights theory needs
to be refocused to include an emphasis on the role played by capital markets, voting, and economic pressures on
incentives among various types of firms. Recent evi- dence, therefore, supports our view with regard to the
expectation of the coefficient for Public.
The second variable, Urban, is also a binary variable equal to one for institutions located in urban settings,
and zero otherwise. Our model contains independent variables we view as representative of various external
pressures on the behavior of the managers presidents of the firmsinstitutions in our sample. Institutions in
large cities and urban areas often receive financial sup- port in the form of scholarships, endowed professorships,
and building funds from corporations and business enterprises as a form of goodwill or even utility maxim-
ization on the part of managers of these firms. There- fore, we expect that corporations and businesses located
in the urban area may have incentives to police and detect inefficient managerial behavior and attempt to
remove inefficient managers Presidents from office. It may also be the case that financial contributors find it
too costly to monitor universities, and may use their influence only during the hiring process and not the
evaluation and firing processes. Therefore, we offer no expectation regarding the sign of Urban.
54 F.G. Mixon Jr, R.W. McKenzie Economics of Education Review 18 1999 51–58
The third variable, PhD, is a binary variable equal to one for institutions offering PhD programs of any kind,
and zero otherwise. These universities often receive higher levels of state funding on a per-student basis, and
may be policed by legislators, university trustees, fac- ulty, students, etc. more heavily than non PhD-granting
counterparts. It is also possible that higher per-student budgets are the result of greater monopoly power or rent-
seeking efforts by universitiescolleges.
3
Therefore, the sign of PhD is an empirical question. An additional
regressor, Roll, is equal to the size of the full-time stud- ent body in 1994 see America’s Best Colleges. We real-
ize that the size of institutions often changed over the history of each institution, however we argue that the
size differences among institutions in our sample was relatively undisturbed over the sample period. Because
larger institutions are more likely to have multiple layers of administration with more institutionalized decision-
making, thus reducing managerialpresidential responsi- bility for errors, it is likely that Roll will retain a positive
coefficient, ceteris paribus.
4
A price variable, AR, is average revenue for each institution in the sample for 1994. It is obtained by divid-
ing total tuition receipts by the size of the student body, and avoids tuition differences for in-state and out-of-
state students at certain institutions. We also make the same argument regarding relative changes versus absol-
ute changes for AR and any other independent variables that are measured with 1994 figures. We expect that AR
serves as a proxy for school quality which presidents have scope for control over and are rewarded for quality
maintenance, so that AR is expected to retain a positive sign, ceteris paribus. It is also likely that students who
pay more will reward more efficient management behavior.
FA denotes the percent of an institution’s student body that receives financial aid from any source for 1994. FA
represents, potentially, a source of managerial policing from the financial services sector, state governments, and
even the federal government through administration of financial aid from these various sources. These insti-
tutions may be more aware of the managerial practices of universities where FA is high, and may exert pressure
accordingly. However, government state and federal bureaucracies are political firms also, and may fail to
exert the type of influence that will promote efficiency.
3
Couch et al. 1992 show that a loophole in Alabamas Code of Ethics, which allows sitting state legislators to be on
the payroll of public colleges and universities, gives incentives for participating legislators to act as brokers by providing an
estimated 19 in additional public funding for every 1 received in salary.
4
Data on the number of administrators per student would be a better measure here, however we were unable to obtain this
information for much of our sample.
Therefore, the expectation regarding FA is ambiguous. Finally, ACT is included on the right-hand side, and
measures the average ACT score for incoming freshmen. ACT likely represents pressure from students — as with
the variable AR, good students national merit scholars and honors students will recognize and punish reward
inefficient managerial behavior. Therefore, we expect that ACT will retain a negative sign. All data for the
independent variables are measured in 1994 values and come from America’s Best Colleges.
5
4. Statistical results
