Directory UMM :Data Elmu:jurnal:E:Economics of Education Review:Vol18.Issue1.Feb1999:

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Managerial tenure under private and government ownership:

the case of higher education

Franklin G. Mixon Jr

a,*

_{, Russell W. McKenzie}

a_{Department of Economics and International Business, University of Southern Mississippi, Box 5072, USM Station, Hattiesburg,}

MS 39406-5072, USA

b_{Department of Economics, Oklahoma State University, Stillwater, OK 74078, USA}

Abstract

The present paper offers statistical evidence which suggests that managers of firms in the higher education industry in the United States (universities and colleges) pursue a variety of goals consistent with economic theory in the context of firm ownership, and that the tenure of managers (university/college Presidents) in this industry differs according to the firm’s organizational structure (public vs. private). The essentially non-transferable property rights (regarding government-owned firms) reduce incentives to police and detect managerial (in)efficiencies. Managers, therefore, face incentives to create internal decision-making processes which increase job security and tenure, along with other non-pecuniary sources of income and utility. Empirical results presented here point out that,ceteris paribus, the average tenure of public university presidents is about five years longer than their private counterparts, as a result of the disparity in incentive structures. [JELD23, I21, I22, L33]1998 Elsevier Science Ltd. All rights reserved.

1. Introduction

In 1991, economist Ronald H. Coase was recognized for his seminal work on the role of firms in the market process (among other ideas) with the Nobel Prize in economic science. Coase pointed out that by forming such an organization (a firm) and allowing some auth-ority (an entrepreneur/owner) to direct the resources, cer-tain costs associated with the market process (i.e., trans-actions costs) are saved. A firm, therefore, consists of a

system of relationshipswhich comes into existence when the direction of resources is dependent on an entrepren-eur (Coase, 1937). Coase’s work is important because it was the first to deal with individual incentives within firms, and resulting firm behavior. Coase dealt explicitly with how firm size affects these relationships and thus

* Corresponding author. Tel.: 5083; Fax: 601-266-4920; E-mail: [email protected]

firm behavior, and his work forms the basis for studies regarding ownership structures and the performance of the firm. Since the seminal work of Coase more than 50 years ago, economists have continued to supplement his early theoretical insights with theoretical extensions, theoretical caveats, and empirical work. One of the most interesting extensions has been the theoretical and empirical work of economists such as DeAlessi (DeAlessi, 1967, 1969, 1974a, b) and Crain and Zard-koohi (1978), who analyze the differences in firm behavior across different organizational structures. They point out that the costs of transferring ownership shares differ among private firms, regulated firms, and political (public) firms. The non-transferable property rights inherent in public ownership reduce attempts to police and detect managerial behavior, leading to many con-cerns of inefficiency within political firms. With statisti-cal data from electric utilities, water and other industries, these economists show a marked difference consistent with microeconomic theory between the level of

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efficiency and the behavior of a firm’s manager (executive leadership) across an array of ownership structures.

The present paper offers an extension to the literature in this area. We offer statistical evidence which suggests that managers of firms in the higher education industry in the United States (universities and colleges) pursue a variety of goals consistent with economic theory in the context of firm ownership, and that the tenure of man-agers (university/college Presidents) in this industry dif-fers according to the firm’s organizational structure. Our results add to and update the body of empirical work in this field of industrial organization.

2. The hypothesis

Excessive costs (or deviations from profit maximization) can be induced by government regulation of firms or government ownership of firms within indus-tries. In transportation, for example, economists have suggested that there is reason to believe that regulations distort producers’ incentives, leading to deficient cost control (“expense-preference” behavior) and excessive investment (see Kahn, 1988). The seminal work in this area of industrial economics includes Averch and John-son (1962); Wellisz (1963), and the excessive investment referred to above is known as the Averch–Johnson effect.1 _{A more recent survey of developments is}

pro-vided by Sherman (1985). In regulated firms, cost mini-mization (and thus standard, rational profit-maximizing behavior) is not the objective; instead, managers maxim-ize their own utility in more costly ventures. These may include luxurious offices and support staff, limousine services, and country club memberships, to name just a few. In fact, the economics literature is replete with stud-ies detailing various alternative firm goals (see Machlup, 1967; Jensen and Meckling, 1976; Fama, 1980). These goals include sales maximization, staff/support maximiz-ation, growth maximizmaximiz-ation, and satisficing behavior.

According to DeAlessi (1974a), one of the alleged implications of the utility-maximization hypothesis is that government decision-makers enjoy longer tenure than their counterparts within privately owned firms. Individual decision-makers are hypothesized to maxim-ize a utility function whose variables reflect a variety of goals such as health, security, prestige, and the welfare of others, to name a few (see also DeAlessi, 1967). The utility-maximizing manager has the incentive to increase the size and duration of all job-related pecuniary and

1_{The work of Averch–Johnson has been challenged by some} economists. Dechert (1984) points out that regulated firms may fear the possibility that regulatory agencies will expropriate any sunk capital investment through tighter regulatory controls.

non-pecuniary streams of income (DeAlessi, 1974a: 646). While managers of privately owned firms might achieve these ends through attempts to increase the value of marginal product, managers of political enterprises often pursue increases in the size of the budget to be administered and the size of the support staff (with “like-able” characteristics), and more pleasant working con-ditions (Niskanen, 1971, 1975). Allowance of such prac-tices by the firm’s owners depends upon enforcing contracts and the effectiveness of internal controls (see DeAlessi, 1974a: 646).

The main difference between private and government-owned firms lies in the relatively higher cost (for polit-ical firms) of transferring ownership shares, which can be achieved only by moving to another state (district, borough, etc.) or by getting involved within the political system (seeking office, voting, etc.). Such essentially

non-transferable property rights reduce behaviors that detect and police managerial efficiency. If differences in property rights associated with alternative ownership arrangements present the choosers (managers) with dif-ferent opportunity sets and, thus, with difdif-ferent cost– reward structures, then the resulting decisions will differ systematically (DeAlessi, 1974b: 2). This implies that managers of political firms have greater opportunity to increase their own welfare at the expense of the employer’s wealth (DeAlessi, 1974a: 646–647). In fact, as DeAlessi points out, managers of government firms, particularly those endowed with a politically influential clientele, can survive and even prosper in the presence of persistent deficits and significant economic losses. These managers have greater opportunities for utility-maximiz-ing behavior.

Salaries (pecuniary returns) of managers in political firms are often more constrained (through statutory ceilings) than those of their private counterparts. Joskow et al. (1996) provide statistical evidence of CEO com-pensation across 87 state-regulated electric utilities (for 1978–1990) which points out the political pressures on executive pay. For instance, CEO compensation is inversely related to utility rate trends over time and how “consumer-friendly” the regulatory climate is within which the utility operates. These results imply that the opportunity cost of nonpecuniary sources of utility is lower. Therefore, the manager will find that the opport-unity cost of activities designed to enhance the prob-ability of survival in office is also lower, and he will have greater opportunity to increase the present value of his pecuniary and nonpecuniary sources of income by increasing his job security. Such managers will have greater incentives and opportunity to reward subordi-nates for loyalty, to institutionalize decision-making in order to dissipate responsibility for errors, and to insti-tutionalize tenure (DeAlessi, 1974a: 647). These points are substantiated by Joskow et al. (1996), who point out that the appointment process and tenure rules also are

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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:

Tenure5a01b1Public1b2Urban1b3PhD (1)

1b4Roll1b5AR1b6FA1b7ACT1ei

The dependent variable above, Tenure, is equal to the current year of our data set (1994) minus the year in which the university/college in the sample was founded (the number of years the institution has existed), divided by the number of presidents (managers) that the University/College 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(fromU.S. News and World Report, 1994). Data on the number of presidents for each university/college in the sample comes from microfiche forms of each university/college 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 (see5_{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 tenure/job security. One potential problem here is the

possibilitythat 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 firms/institutions 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.

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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 (seeAmerica’s Best Colleges). We real-ize that the sreal-ize 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 managerial/presidential 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 Alabama"s} 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 (in)efficient 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 fromAmerica’s Best Colleges.5

4. Statistical results

The fully specified model was run in OLS along with several more parsimonious versions for comparison. These results are included below in Table 2. Of the seven regressors included in model (1), three are significant at the 95% level of confidence. The main regressor, Public, retains the expected positive sign and is significant at the 99% level of confidence in all versions of the equation. In fact the parameter estimate for Public ranges from 4.68 to 4.85, suggesting that the average tenure of presi-dents at public institutions is approximately 5 years greater than their private counterparts, ceteris paribus. This significant finding supports the main premise of the work of DeAlessi and Crain and Zardkoohi detailed above which suggests that managerial tenure under government ownership is a source of income/utility with significant present value, and public executives will organize production and decision-making in a manner that rewards subordinates for loyalty while dissipating executive responsibility for inefficiencies. These results suggest that university trustees and legislators should consider compensation packages that provide incentives for managerial efficiency.

Of the other regressors in version (1), ACT is negative and significant and AR is positive and significant, which are expected results. The main results are consistently found in other versions of Eq. (1), when some of the regressors are deleted for comparison. The regressionR -square values range from 19.1% to 21.2%, as pointed out at the bottom of Table 2. Version (1) of Table 2 is also employed to provide estimates forpredictedaverage managerial tenure across the sample of 73 institutions. These predictions are presented in Appendix A, and are compared with the actual mean presidential tenure for each institution.

5_{We realize that the use of such end-year (1994) data is a} limitation of our test, because the factors influencing the tenure of presidents have changed over time for each university in the sample. Although our data do not precisely capture these changes, we do argue that stable relative values (from school to school) allow us to capture some of the important relationships.

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Table 2

Summary of OLS results. Dependent variable: tenure

(1) (2) (3) (4)

intercept 13.88*** 13.79*** 13.63*** 13.78***

(2.54) (2.54) (3.35) (3.41)

Public 4.68*** 4.82*** 4.85*** 4.80***

(2.79) (2.94) (3.24) (3.23)

Urban 0.41 0.43 0.44

(0.46) (0.47) (0.49)

PhD 21.76 21.43 21.43 21.39

(21.49) (21.45) (21.46) (21.44)

Roll 0.07E23

(0.51)

AR 0.06E22*** 0.06E22*** 0.06E22*** 0.06E22***

(3.94) (3.99) (4.09) (4.09)

FA 0.002 20.001

(0.07) (20.05)

ACT 20.43** 20.40* 20.40** 20.39**

(22.01) (21.95) (22.04) (22.01)

nobs 73 73 73 73

F-statistic 2.50** 2.91*** 3.54*** 4.07***

R-square 0.212 0.209 0.209 0.191

Adj.R-square 0.127 0.137 0.150 0.144

Numbers in parentheses representt-values for the OLS regression; ***[**] represents significance at the 99%[95%] level of confi-dence.

As a further test of the hypothesis, a new dependent variable, "Pres" was created which denotes the number of presidents that each of the 73 institutions have had throughout their history. Because this variable does not adjust for age, "Found" or the year in which the insti-tution was founded is included as a right-hand side vari-able. This variable is expected to be negatively related to the new dependent variable Pres. Each of the other regressors is included, and each is expected to retain the

oppositecoefficient from Eq. (1) above. Also, because the dependent variable represents discrete values only, this hypothesis is modeled with a maximum likelihood procedure (tobit). The results are provided in Table 3.

The evidence provided in Table 3 provides further (and perhaps stronger) support for the hypothesis developed by DeAlessi and Crain and Zardkoohi. Five of the eight regressors are significant at the 95% level of confidence, and none retains an unexpected sign. The model suggests that public institutions have fewer presi-dents over their lifespan, as expected, and that external institutions (FA) and students (ACT) possibly serve as pressure groups on the managerial efficiency of univer-sities and colleges. In sum, the statistical evidence sup-ports the view that job tenure is an important source of pecuniary and non-pecuniary income/utility for presi-dents (managers) of institutions of higher learning in the United States. From a public policy perspective, execu-tive compensation schemes that reward cost savings, attracting quality students, and other amenities may work

Table 3

Empirical results of maximum likelihood tobit procedure. Dependent variable: Pres

Variable Coefficient t-ratio

Intercept 14.97 6.86***

Public 20.42 22.97***

Urban 20.04 20.55

PhD 0.04 0.36

Roll 0.1E24 1.16

AR 20.4E24 23.05***

FA 0.5E22 2.00**

ACT 0.04 2.05**

Found 20.01 26.78***

Log-likelihood 218.54

Note: ***[**] denotes significance at the 99%[95%] level of confidence.

to promote efficiency. Current arguments for privatiz-ation or direct government subsidies to prospective stu-dents in the form of vouchers also provide avenues to explore which also result in incentives that lead to efficiency gains.

5. Concluding comments

This article presents evidence which suggests that managers of political firms have greater opportunities for

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utility maximization than their private sector counter-parts. Although restricted by statutory salary ceilings, public managers find that the opportunity cost of activi-ties designed to enhance tenure are lower, and will work to ensure survival in office. The evidence presented here suggests that presidents of public universities and col-leges in the United States recognize the different opport-unity sets that they face, and make decisions that work to ensure survivability and tenure. As pointed out by pre-vious studies, where a direct connection between

pro-Appendix A

University/college State Actual mean pres. Pred. mean tenure pres. tenure Hawaii Pacific University HI 9.33 8.29 Northwest Nazarene College ID 8.10 11.27

West Georgia College GA 14.67 12.06

Asbury College KY 7.43 9.02

Georgetown College KY 7.17 8.80

Wichita State University KS 9.00 10.31

Ottawa University KS 8.60 9.98

Drake University IA 11.30 10.03

DePauw University IN 8.72 11.68

Butler University IN 8.18 11.87

St. Joseph’s College IN 7.50 11.08

Purdue University IN 13.89 11.17

Greenville College IL 17.38 10.76

Wheaton College IL 19.14 11.22

Boise State University ID 12.40 12.15

Georgia College GA 13.13 12.38

Paine College GA 8.77 11.43

Morehouse College GA 12.78 8.58

Savannah State College GA 10.40 15.27

Valdosta State University GA 14.67 12.40

Smith College MA 13.67 13.08

Pine Manor College MA 16.60 15.11

Western Maryland College MD 18.29 13.94 Grambling State University LA 24.00 13.51

Bates College ME 23.17 17.58

Northwestern College MN 15.33 11.24

Mississippi College MS 7.00 7.98

William Carey College MS 12.57 9.55

Miss. Univ. for Women MS 9.17 10.86

Miss. Valley State Univ. MS 11.00 10.97 Jackson State University MS 14.62 12.19

Millsaps College MS 11.56 9.90

University of Mississippi MS 10.00 9.58 N.E. Missouri State Univ. MO 9.77 10.23 Fairleigh Dickinson Univ. NJ 10.40 9.94

Drew University NJ 6.60 12.34

University of New Mexico NM 7.00 10.20 ductivity and reward is severed, as in public enterprises where tenure maximization is practiced, production will be less economically efficient.

Acknowledgements

The authors thank Louis DeAlessi, Steve Caudill and two anonymous referees of this journal for helpful com-ments. Any remaining errors are our own.

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University/college State Actual mean pres. Pred. mean tenure pres. tenure

Keene State College NH 10.63 14.51

Wake Forest University NC 13.33 9.53

Salem College NC 12.33 10.61

Wittenberg University OH 13.81 12.96

Ohio Northern University OH 12.11 11.20

University of Toledo OH 9.38 10.69

So. Oregon State College OR 17.86 11.66 Portland State University OR 8.00 10.57

Pacific University OR 9.67 10.19

Phillips University OK 11.00 10.04

Bloomsburg State Univ. PA 8.61 12.32

South Carolina State Univ. SC 14.00 11.46

Augustana College SD 6.38 10.96

Austin Peay State Univ. TN 9.57 12.02

University of North Texas TX 8.67 9.06

Texas Tech University TX 6.45 9.74

Southern Methodist Univ. TX 7.55 9.67

Middlebury College VT 12.93 15.64

Walla Walla College WA 5.10 10.08

West Virginia Inst. of Tech. WV 10.43 12.67

Mount Mary College WI 16.20 10.89

Pomona College CA 13.38 14.86

Arizona State University AZ 7.27 11.53 Univ. of Southern Mississippi MS 14.00 10.72

Mount Vernon College DC 14.87 12.63

Albertus Magnus College CT 5.38 11.61

Connecticut College CT 10.38 15.17

Florida International Univ. FL 5.80 9.44

University of Miami FL 13.80 12.98

Florida A and M University FL 13.37 12.04 University of West Florida FL 10.33 12.04

Bluefield College VA 10.57 14.18

Radford University VA 11.57 11.83

James Madison University VA 21.50 11.95

Ferrum College VA 9.00 11.81

Lynchburg College VA 11.37 12.65

References

Averch, H., Johnson, L.L., 1962. Behavior of the firm under regulatory constraint. American Economic Review 52, 1052–1069.

Coase, R.H., 1937. The nature of the firm. Economica 4, 386–405.

Couch, J.F., Atkinson, K.E., Shughart II, W.F., 1992. Ethics laws and the outside earnings of politicians: the case of Ala-bama’s "legislator-educators". Public Choice 73, 135–145. Crain, W.M., Zardkoohi, A., 1978. A test of the property-rights

theory of the firm: water utilities in the United States. Jour-nal of Law and Economics 21, 395–408.

DeAlessi, L., 1967. A utility analysis of post-disaster cooperation. Papers on Non-Market Decision-Making 3, 85–90.

DeAlessi, L., 1969. Implications of property rights for

govern-ment investgovern-ment choices. American Economic Review 59, 13–24.

DeAlessi, L., 1974a. Managerial tenure under private and government ownership in the electric power industry. Jour-nal of Political Economy 82, 645–653.

DeAlessi, L., 1974b. An economic analysis of government own-ership and regulation. Public Choice 19, 1–42.

Dechert, W.D., 1984. Has the Averch–Johnson effect been theoretically justified? Journal of Economic Dynamics and Control 8, 1–17.

Fama, E., 1980. Agency problems and the theory of the firm. Journal of Political Economy 88, 288–307.

Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs, and ownership structure. Journal of Financial Economics 18, 305–360.

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con-straints on executive compensation: evidence from the elec-tric utility industry. Rand Journal of Economics 27, 165– 182.

Kahn, A. E. (1988)The Economics of Regulation. MIT Press, Cambridge, MA.

Machlup, F., 1967. Theories of the firm: marginalism, behavioral and managerial. American Economic Review 57, 1–33.

Mobley, L.R., 1997. Multihospital chain acquisitions and com-petition in local healthcare markets. Review of Industrial Organization 12, 185–202.

Niskanen, W. A. (1971) Bureaucracy and Representative Government. Aldine-Atherton, Chicago, IL.

Niskanen, W.A., 1975. Bureaucrats and politicians. Journal of Law and Economics 18, 617–643.

Peters, L.L., 1993. For-profit and non-profit firms: limits of the simple theory of attenuated property rights. Review of Industrial Organization 8, 623–633.

Sherman, R., 1985. The Averch and Johnson analysis of public utility regulation twenty years later. Review of Industrial Organization 2, 178–193.

Toma, E.F., 1990. Boards of trustees, agency problems and uni-versity output. Public Choice 67, 1–9.

Toma, E.F., 1986. State university boards of trustees: a princi-pal–agent perspective. Public Choice 49, 155–163. Wellisz, S.H., 1963. Regulation of natural gas pipeline

compa-nies: an economic analysis. Journal of Political Economy 71, 30–43.

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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:

Tenure5a01b1Public1b2Urban1b3PhD (1) 1b4Roll1b5AR1b6FA1b7ACT1ei

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 (see5_{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

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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 universities/colleges.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 (seeAmerica’s Best Colleges). We real-ize that the sreal-ize 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 managerial/presidential responsi-bility for errors, it is likely that Roll will retain a positive coefficient,ceteris paribus.4

3 _{Couch et al. (1992) show that a loophole in Alabama"s}

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.

4. Statistical results

5_{We realize that the use of such end-year (1994) data is a}

limitation of our test, because the factors influencing the tenure of presidents have changed over time for each university in the sample. Although our data do not precisely capture these changes, we do argue that stable relative values (from school to school) allow us to capture some of the important relationships.

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Table 2

Summary of OLS results. Dependent variable: tenure

(1) (2) (3) (4)

intercept 13.88*** 13.79*** 13.63*** 13.78***

(2.54) (2.54) (3.35) (3.41)

Public 4.68*** 4.82*** 4.85*** 4.80***

(2.79) (2.94) (3.24) (3.23)

Urban 0.41 0.43 0.44

(0.46) (0.47) (0.49)

PhD 21.76 21.43 21.43 21.39

(21.49) (21.45) (21.46) (21.44)

Roll 0.07E23

(0.51)

AR 0.06E22*** 0.06E22*** 0.06E22*** 0.06E22***

(3.94) (3.99) (4.09) (4.09)

FA 0.002 20.001

(0.07) (20.05)

ACT 20.43** 20.40* 20.40** 20.39**

(22.01) (21.95) (22.04) (22.01)

nobs 73 73 73 73

F-statistic 2.50** 2.91*** 3.54*** 4.07***

R-square 0.212 0.209 0.209 0.191

Adj.R-square 0.127 0.137 0.150 0.144

Numbers in parentheses representt-values for the OLS regression; ***[**] represents significance at the 99%[95%] level of confi-dence.

Table 3

Empirical results of maximum likelihood tobit procedure. Dependent variable: Pres

Variable Coefficient t-ratio

Intercept 14.97 6.86***

Public 20.42 22.97***

Urban 20.04 20.55

PhD 0.04 0.36

Roll 0.1E24 1.16

AR 20.4E24 23.05***

FA 0.5E22 2.00**

ACT 0.04 2.05**

Found 20.01 26.78***

Log-likelihood 218.54

Note: ***[**] denotes significance at the 99%[95%] level of confidence.

5. Concluding comments

This article presents evidence which suggests that managers of political firms have greater opportunities for

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pro-Appendix A

University/college State Actual mean pres. Pred. mean

tenure pres. tenure

Hawaii Pacific University HI 9.33 8.29

Northwest Nazarene College ID 8.10 11.27

West Georgia College GA 14.67 12.06

Asbury College KY 7.43 9.02

Georgetown College KY 7.17 8.80

Wichita State University KS 9.00 10.31

Ottawa University KS 8.60 9.98

Drake University IA 11.30 10.03

DePauw University IN 8.72 11.68

Butler University IN 8.18 11.87

St. Joseph’s College IN 7.50 11.08

Purdue University IN 13.89 11.17

Greenville College IL 17.38 10.76

Wheaton College IL 19.14 11.22

Boise State University ID 12.40 12.15

Georgia College GA 13.13 12.38

Paine College GA 8.77 11.43

Morehouse College GA 12.78 8.58

Savannah State College GA 10.40 15.27

Valdosta State University GA 14.67 12.40

Smith College MA 13.67 13.08

Pine Manor College MA 16.60 15.11

Western Maryland College MD 18.29 13.94

Grambling State University LA 24.00 13.51

Bates College ME 23.17 17.58

Northwestern College MN 15.33 11.24

Mississippi College MS 7.00 7.98

William Carey College MS 12.57 9.55

Miss. Univ. for Women MS 9.17 10.86

Miss. Valley State Univ. MS 11.00 10.97

Jackson State University MS 14.62 12.19

Millsaps College MS 11.56 9.90

University of Mississippi MS 10.00 9.58

N.E. Missouri State Univ. MO 9.77 10.23

Fairleigh Dickinson Univ. NJ 10.40 9.94

Drew University NJ 6.60 12.34

University of New Mexico NM 7.00 10.20

ductivity and reward is severed, as in public enterprises where tenure maximization is practiced, production will be less economically efficient.

Acknowledgements

The authors thank Louis DeAlessi, Steve Caudill and two anonymous referees of this journal for helpful com-ments. Any remaining errors are our own.

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University/college State Actual mean pres. Pred. mean tenure pres. tenure

Keene State College NH 10.63 14.51

Wake Forest University NC 13.33 9.53

Salem College NC 12.33 10.61

Wittenberg University OH 13.81 12.96

Ohio Northern University OH 12.11 11.20

University of Toledo OH 9.38 10.69

So. Oregon State College OR 17.86 11.66

Portland State University OR 8.00 10.57

Pacific University OR 9.67 10.19

Phillips University OK 11.00 10.04

Bloomsburg State Univ. PA 8.61 12.32

South Carolina State Univ. SC 14.00 11.46

Augustana College SD 6.38 10.96

Austin Peay State Univ. TN 9.57 12.02

University of North Texas TX 8.67 9.06

Texas Tech University TX 6.45 9.74

Southern Methodist Univ. TX 7.55 9.67

Middlebury College VT 12.93 15.64

Walla Walla College WA 5.10 10.08

West Virginia Inst. of Tech. WV 10.43 12.67

Mount Mary College WI 16.20 10.89

Pomona College CA 13.38 14.86

Arizona State University AZ 7.27 11.53

Univ. of Southern Mississippi MS 14.00 10.72

Mount Vernon College DC 14.87 12.63

Albertus Magnus College CT 5.38 11.61

Connecticut College CT 10.38 15.17

Florida International Univ. FL 5.80 9.44

University of Miami FL 13.80 12.98

Florida A and M University FL 13.37 12.04

University of West Florida FL 10.33 12.04