Introduction
Resource management is a critical component of the role of a Provost/VPAA. Within the academic affairs budget, the largest component by far is allocated to faculty salaries. It is also the least flexible resource pool as funds cannot easily be moved into or out of other accounts, mainly because of the associated benefits costs, which, depending on your institution’s accounting practices, may or may not be included in the salary account (within SUNY for example, benefits were managed and paid for at the system level).
Throughout the year, as faculty resign, take disability leave, retire, or die, the number of faculty lines funded from this account can fluctuate. In addition, from year to year, the overall funding may change. If your institution is growing, you may be able to convince the President to allocate funding from the general fund (tuition/state support) for new faculty lines. Of course, the opposite may be true if the institution is shrinking. These changes offer opportunities for strategic re-allocation of faculty lines. The more flexible your policy is on line retention, the greater the strategic opportunities. The most flexible policy requires that all open lines roll up to the Provost for allocation. This means that lines can be moved between departments and even colleges. A less flexible policy might require they roll up to the Dean and can thus be re-allocated across departments within a college. The most popular with faculty, as they often feel entitled to replace open faculty positions automatically, but least flexible policy strategically, is that they remain within the department. When lines are moved, or new lines are allocated, it is therefore important that the placement decision be based on an equitable process and supported by good data. Determining the relative priority for allocating lines is challenging, usually based on a complex set of factors, and the major focus of this article. Note, this article focuses on instructional needs and does not factor research expertise into the analysis.
Once the decision is made to allocate a new or replacement line to a department additional factors must be considered. Should it be a tenured/tenure track (T/TT), non-tenure track multiyear or yearly contract (NTT), or a temporary hire? Temporary hires are usually used after a failed search, to give the department more time to do an extended search, or to postpone a long-term commitment until its need had been clarified. At what rank should the department be authorized to hire the position? If a position is not being replaced, do funds need to be allocated to the department to hire adjuncts (ADJ) to cover courses? These decisions beg the question that is also a focus of this article – what is the appropriate mix of T/TT to NTT to ADJ positions, and of full to associate to assistant professors and instructors, within the departments?
The analytical approach addressed in this article is embodied in a companion spreadsheet that has been used to conduct the analysis at multiple institutions. It contains real data, so readers can get a sense of the measures, and is referred to throughout the document by referencing specific columns and tabs. Readers are encouraged to have the spreadsheet open as they read the article, and to download, use, and modify it for their own use. As with all data-based decision making, there is always a backstory to the data, especially the human element, which should be considered as part of the process. Data is flour for the bread – other ingredients and extensive kneading are needed before good decisions can be made.
Determining Funding/Line Allocation
The model described in this paper for managing faculty line allocation is essentially based on the assumption that the number of credits delivered by adjuncts and overloads within a department is the bellwether for hiring needs – if there is a high proportion of courses delivered by adjuncts for example, then it is probably time for a new full-time (FT) hire. For this to be true, the model requires that FT faculty are teaching their contracted load or have legitimate course releases for other activities (this should be monitored, a process for their authorization established, and defined budgets established), and that course scheduling and enrollments are being managed efficiently.
Efficient Management of Existing Human Resources Within Departments
Funding for faculty lines should generally not be allocated to a department that is not using its current human resources efficiently. For example, a department may have full loads assigned to all faculty, but it may be offering courses every semester at 50% enrollment whereas it could offer the courses every other semester with 100% enrollment. If it accomplished this, additional courses could be assigned to the FT faculty within load, and new positions would not need be hired or overloads funded. It is also possible that a department is trying to offer more courses than are necessary to serve the degree, perhaps to serve the interests of the faculty. A better balance between program needs and faculty interests might result in higher per course enrollments.
Scheduling efficiency within a department, a responsibility of the chair, can be measured by the average, percent of course caps (Avg % Caps). If a course has a cap of 20 and 15 students are enrolled on census day then the % cap is 75%. Averaging this measure across all the courses offered by the department in a year produces the Avg. % Caps. Recognizing that some courses will have to run with low enrollment to help students make satisfactory progress toward graduation, Avg % Caps will rarely approach 100%. However, some courses will actually have a % cap greater than 100%, if the instructor allows students to enroll even though the course is technically full and has exceeded its cap. This can offset other courses that are not fully enrolled. Avg % Caps is a very good comparative measure of the efficiency of scheduling and enrollment management across departments and should be a conversation between deans and chairs at least once a year. In general, measures greater than 85% indicate a well-managed process in the authors experience.
This metric assumes that appropriate course caps are set for each course. In the authors experience course caps have been changed dynamically by faculty at their will, some even being set to zero so that all students are required to get the permission of the instructor to enroll. Caps are also very discipline specific and can depend on the capacity of the room in which the course is delivered – labs in particular are often subject to this constraint. In addition, factors like writing and math intensivity may affect the cap size. Setting caps is therefore a complex equity issue with respect to faculty workload and worthy of close examination and policy formation. The group that deals with this issue on a regular basis is the department chairs, and it is therefore appropriate to charge a task force made up predominantly of chairs to make recommendations to the administration on course caps and faculty teaching workload (see related article on Developing Equitable and Effective Faculty Teaching, Advising, Service and Research Workload Policy and Practices). Once this policy is created, the chair should be held responsible, or require approval from the dean to deviate from the caps set for their courses. Ideally, caps should be set by the registrar at the chairs request and not modifiable by individual faculty. With these policies in place % Avg Cap becomes a reliable measure of efficient use of faculty resources and should be the first metric examined when considering a department’s faculty needs. The multiyear trend in this metric can help determine if departments efforts to improve this score are working (see columns E thru G in the spreadsheet).
Tying Adjunct Hires and Overloads to Market-Based Demand for Courses
Once efficient course management and faculty workloads are established and monitored, it should be relatively easy for the Provost to convince the President and the Cabinet to create a policy that, should there be enough students needing to take a course (as measured by data based enrollment projections, wait-lists of other measures) to make it financially viable to run a net new section, that it should automatically be offered and funded from the general fund (after all that is where the net new tuition will end up). This makes sense since the tuition revenue will equal or exceed the expenses and it aids in student success.
Since overload/adjunct costs and other course expenses can vary by discipline, and since tuition can also be course specific (grad vs undergrad, differential tuition, etc.) it may be helpful to define in advance a breakeven enrollment number for courses, in addition to appropriate cap size. Some institutions have policy that proportionally reduces the overload/adjunct pay for a course if the enrollment falls below the breakeven number. If this is the case any course is viable if the instructor is willing to accept the resultant pay. These policies create an unrestrained marked-based economy in which course offerings are determined by demand and not constrained by academic budgets or poor planning. In the authors experience this has resulted in explosive growth in some departments.
As program enrollments grow, it is appropriate to absorb the additional instructional demand through adjunct and overload teaching assignments as shown between 2001 and 2003, and between2005 thru 2007 in Figure 1. This provides the department with the flexibility to adjust to temporary surges in enrollment without having to commit to a FT hire. At some point, if the enrollment growth proves to be persistent over multiple semesters then a FT hire may be justified (as shown in 2004 and 2008 in Fig. 1). This shifts the teaching load from the adjunct pool to the FT faculty. Similarly, if enrollments are dropping it may be appropriate to eliminate a FT position and replace it with ADJ/OVLD instruction if necessary. It is therefore important to adjust the department’s adjunct budget as FT position numbers change. Adjunct salary savings will offset the salary cost for the new hire, and FT salary savings can fund additional adjuncts if necessary.
Determining Line Type
Once the decision has been made that a department needs to have the number of its FT instructional positions changed, the next decision is the type of position(s) that should be allocated or unallocated. Faculty appointment types vary primarily by the length of commitment by the institution to the position. T/TT positions represent an average commitment of approximately 35 years (factoring in resignations and deaths), NTT multi-year or renewing contracts are usually five years or less, and adjunct appointments are usually a single semester long. When creating a new T/TT position the institution is taking a gamble that over the long term there will be enough students and instructional load to justify the appointment. NTT appointments represent a lower shorter-term risk, and ADJs no risk at all since they are usually hired to serve an immediate and guaranteed instructional demand. From a risk management perspective, a mixture of these three appointment types within a department provides the best balance between attracting and retaining the best faculty, and the flexibility to respond to changing enrollments.
The relative ratio of T/TT to NTT to ADJ/Overloads full time equivalent (FTE) lines within a department has a big impact on the quality of programs, time faculty can spend with students outside of class, advising, scholarship, and overall faculty participation in the governance process. In this article, course assignments beyond the normal contracted teaching load (overloads), are pooled with adjunct teaching loads, as they generally reduce the effectiveness of full time faculty in these other areas of responsibility. They also fit within the adjunct hires workload hours as they are low risk investments. This creates the Adj/Ovlds measure. For determining ratios, the Adj/Ovlds credit hours can be divided by the standard annual teaching load for full-time positions (e.g. 12) to generate an appropriate number of FTE positions dedicated to this.
The ratio of FT positions (T/TT plus NTT) to Adj/Ovlds FTEs is particularly critical, as the lower this ratio is the more time FT faculty must spend managing adjunct faculty. It should be recognized that this is an exponential relationship, since replacing each FT position with adjunct instruction results in the hire of at 2 adjunct positions on average. A dwindling FT faculty must therefore manage a disproportionately growing adjunct group. At one institution where the author worked 70% of the instruction was delivered through a combination of adjunct instruction or overloads. FT faculty constantly complained they had little time to focus on anything but instruction and adjunct management. It was also very difficult to get faculty to serve on committees and scholarly activity was very challenging.
The relative ratios of T/TT to NTT to ADJ/OVLD FTEs may to some degree be imposed by external constraints. Some accrediting bodies, AACSB for example, define very precisely the minimum proportion of FT and Adjuncts that a program must have to be meet its standards. Central administration of university systems may also impose rules – for example, the State Council for Higher Education in Virginia (SCHEV) requires that no more than 30% of a programs instructional hours be delivered by adjuncts. However, most departments have no constraints and indeed most do not even measure these ratios. How then does one determine what the ratios should be?
In a department in which programs and enrollments are consistently growing, the risk of appointing T/TT positions is low and their proportion within the faculty can therefore be high. For declining departments, the opposite is true, as FT faculty lines may need to be removed from a department in the near future. Table 1 shows how this approach can be extrapolated to set the relative proportions of faculty type based on risk.
The designation of three bands is arbitrary, but their number should be limited as the model can get very complex otherwise. At some point actual target percentages should replace the high, medium and low metrics, but this should wait until an analysis of all departments has been completed and data on current ratios at the college and institutional level are available, as one may be surprised by how different reality is from abstract ideals. One can then set strategic targets that one would like to achieve over the long term for the institution, and for departments falling within each risk band. Table 2 shows actual data from this analysis for each class of growth rate, the then current distribution of faculty types, and the strategic targets that were set.
It should be recognized that shifting institutional faculty type ratios by just a few percentage points can involve significant funds, especially if the institution wants to change the relative proportion of T/TT positions.
Determining Departmental Growth Rates and Associated Risk Categories
Student credit hour (SCH) generation by a department is the most robust measure of productivity and is relatively easy to measure. It is better than credit hours generated, or courses delivered, as it factors in enrollments and is directly proportional to the revenue generated through tuition. Growth rate can be defined as the slope of the linear regression analysis for the SCH production over a period of years as shown in Figure 2. A three-year window of analysis is reliable and responsive enough to recent changes (addition of new degrees for example) to support decisions. Deans have argued for a five-year window, but the author believes that this can blunt recent changes too much. 
However, this data is provided in the sample analytical spreadsheet for completeness (Cols H thru L). This analysis can produce a wide range of slopes, as shown in Fig. 3 in which the slopes were graphed for all departments at Fort Hays State University.
Once growth rates have been measured they need to be grouped into risk categories. This is a somewhat arbitrary process, but groups may jump out of the analysis. For example, in Fig. 3 there is a cluster of negative slopes that were defined as declining. Static slopes were defined as slightly negative to slightly positive and growing as strongly positive as shown in the figure. Traffic light colors (green, orange and red) were also associated with the three categories in the figure and in the spreadsheet to aid with visualization. This was reinforced by arrows pointing up, sideways and down in the spreadsheet (Columns AZ, BA and DI). Another valid measure is the average percent change in SCH production over the analysis time frame. The difference between this and the slope measure, is that the slope is independent of the overall size of the SCH (it is an absolute measure) whereas the percent change takes this into account (it is a relative measure). The average percent change is shown in column BA of the spreadsheet, and can produce results that are worth contrasting with the slope analysis.
Once one of the three growth rate categories had been assigned to a department, the corresponding strategic target ratios were automatically assigned (see Table 2 above and cols DI thru DL). These percentages were then used to calculate the appropriate FTEs of each appointment type (cols DC thru DE) based on the current number of lines in the department (col BI), and then to determine how current numbers would need to be adjusted (cols Df thru DH) by comparing the current distribution (cols BF thru BH) to the target distribution (cols DC thru DE). Column DB is used to recalculate the FTE distributions if new lines were to be added.
While not directly relevant to the position allocation process, it can be revealing to calculate the SCH production by T/TT, NTT, Adjuncts, Graduate Assistants (GA) and overloads (OL) as shown in columns M thru Q in the spreadsheet. It can also be useful to look at the SCH produced per faculty FTE comparatively across departments (column T and graphed in the [meta-analysis] tab). The most important measure, however, is the percentage of SCH generated by combining adjuncts and overloads as shown in column W. This is so important that is it used as the primary sort column for the spreadsheet. This is because in general, departments with the highest proportion of SCH generated by adjuncts/overloads are under the greatest strain and most in need of new FT faculty lines, assuming of course that their efficiency score is high. Additional metrics that may be useful include overall credit hours generated (cols X and Y), credit hours by faculty type and per FTE (cols Z thru AF and graphed in the [Meta-analysis] tab), credit hour production by faculty type (cols AG thru AI), SCH production for upper, lower, graduate and in-major courses (cols AJ thru AM), the number and trend of majors and degrees awarded (cols AN thru AY).
Modelling Projected Changes
The spreadsheet contains a modelling section (cols CB thru DL) to help make decisions about what type of positions should be assigned to a department. Relative SCH production ratios can be changed by adding or removing net positions within the T/TT (col CB) and NTT (col CC) categories. The model assumes that net changes within these summed FT categories will automatically result in changes to the Adj/OL FTEs. For example, if the FT positions are reduced by one then an FTE of one is automatically added to the Adj/OL category to pick up the courses. An additional option is to convert positions between T/TT and NTT (col CD – the use of a negative 1 indicates a conversion from T/TT to NTT). Since this does not affect the total number of FT positions within the department, the Adj/OL FTEs are not automatically adjusted. Positive and negative numbers can be entered into these three columns and will result in a recalculation of the data shown in cols CP thru DA, thus showing the impact of the proposed change.
Determining the Appointment Rank of New or Reallocated T/TT Positions
Once it has been determined that a new T/TT position should be created in a department, either by conversion or addition, a decision on the appointment rank needs to follow. In many institutions it is assumed that this appointment will be made at the Assistant Professor level, as these positions are the least expensive to hire. Healthy departments have a distribution of faculty ranks since, for example, those that do not have senior faculty can be quite dysfunctional. Deliberately shaping this distribution can be challenging, since faculty promotions are dependent on the individual faculty, their drive and accomplishments. However, a reasonable minimal target ratio can be established to help make decisions when new appointments are made. Following discussion with the Academic Council (Deans and other academic leaders) at FHSU, it was decided that a 1:2:3 ratio (17%:33%:50%) of Full:Associate:Assistant Professors within departments would be a reasonable minimal target. Factors that were considered in determining this ratio were:
- Senior faculty are needed to chair the department and take leadership roles across the university.
- Adequate numbers of tenured faculty should be available to serve on tenure committees.
- Succession planning for chairs, etc., requires senior faculty.
- Having all leaders and no followers was unhealthy.
- Fresh blood and new perspectives are always needed.
- Some faculty will not make it through the tenure process.
In practice, this means that a department that does not meet its target minimum ratio for Full and Associate Professors, is authorized to search at these ranks, even though this incurs additional expense. The spreadsheet shows the FTE breakdown and percent distribution of each of these ranks for each department (cols BM thru BU). This data is then used to determine the appropriate rank of new appointments.
Some Practical Considerations When Attempting to Shape the Faculty Body
It is very easy, assuming funding is allocated, to add T/TT positions to a department, but very difficult to remove them. Reducing their numbers must generally wait until a position becomes vacant by natural attrition. Even then, serious consideration must be given to the faculty expertise that is needed to support the programs before a position is moved or removed. Even though the data may indicate that a FT position could be removed, the department may not be viable without it. Exceptions to this are when enrollment numbers drop to such an extent that majors and minors must be eliminated, or entire departments closed. Even then, university policy may require that tenured faculty be assigned to open positions in other departments if they are qualified.
When positions become available, either through new funding or movement between departments, one of the most difficult decisions is to determine if they are allocated to relieve existing department strain (having a high proportion of courses taught by adjuncts and overloads) or to support new program development. Department strain is generally caused by growth in existing programs, or by having unfilled FT faculty lines for which searches have not be authorized. As administrators, it is tempting to give priority to lines intended to support new program growth, as this increases net new revenue and grows the institution. However, this disincentivizes faculty from putting effort into growing existing programs, as they end up just increasing their own workload. The deciding factor may be whether the investment in new programs will generate sufficient net additional revenue (profit for want of a better word) such that this can be used to relieve strain elsewhere in a couple of years – a rising tide raises all boats. However, at some point a commitment must be made to invest this revenue to relieve strain, if faculty morale and faith in the process is not to be damaged.
A higher proportion of adjuncts teaching in a department may be appropriate in some fields, such as business, where practitioners bring real-World experience to the classroom. On the other hand, a low number of adjuncts may reflect a shortage of available expertise in the local area rather than a lack of need by the department for this group. This can be particularly true for institutions located in isolated rural areas.
When changing faculty numbers within departments it is important to consider the impact on departmental administration. Net new positions may require new offices and furniture. Each position within the department should have operational funding associated with it to support office supplies, phone service, technology costs, faculty travel, etc. In addition, particularly in rapidly growing departments, it is important to have policy in place that scales administrative support and release time/stipends for chairs, assistant chairs and program heads, etc., to keep these proportional to the workload. Ignoring these factors may disincentivize the department and make it reluctant to grow.
The Decision-Making Process
As outlined in the article on Building a World Class Faculty, the Institutional Research department was charged with gathering the data and doing the analysis in each fall semester. Each dean was simultaneously charged with gathering data on expected retirements, resignations, FT positions currently filled with temporary hires, and other positions that were expected to be open in the following academic year. In addition, the deans were expected to work with their departments to gather proposals, with enrollment projections, for new programs (or new delivery formats of existing programs) that would require new faculty positions. These needs and openings were documented in the [2017 positions] tab and in columns BV thru CA of the [Data] tab of the spreadsheet, which was then shared with the Academic Council. To prevent confusion position control numbers (PCNs) were added to the spreadsheet to specifically identify the positions that were open.
Early in the spring term, the Council met and each dean, in a round-robin process, presented their case using the [data] and the graphs provided for each department (see sample [ACTG] tab), to retain or move existing open positions within or to other departments within their college. At the end of each round all the deans scored each presentation between 5 and 1, as shown by the scale on the [2017 Positions] tab. Other members of the council were encouraged to ask questions or challenge the data during each presentation, but only the deans were empowered to score. While the voting results were technically advisory to the Provost who did not vote, the data and cases presented were generally so compelling that the deans votes determined the outcome. The scores of the deans were recorded and averaged in the spreadsheet. If retaining an open position within a college was not supported, it was moved to the general allocation pool which also contained any new lines that had been funded by the President.
Once all existing open lines had been reviewed, a second round-robin process was started in which each dean made the case to have lines from the general allocation pool assigned to a department to support strain relief or new program development. Generally, this was done for a single position at a time, unless multiple simultaneous hires were needed to start a program. Each dean presented based on their priority order, all deans presenting their top priorities before voting at the end of the round. This was repeated until all lines in the general allocation pool were assigned to departments.
For current open positions that the dean believed could be filled by the next fall with a rapid spring search, an immediate search was authorized. For all other positions, the departments were authorized to conduct their searches during the following academic year, with the positions to be filled the succeeding fall.
Transparency
At the end of the process the spreadsheet was shared with all department chairs, who were free to discuss the results and analysis with their faculty. While chairs did not always agree with the results, they could see the metrics on which the decisions were made. It is hoped that sharing these metrics led to a clearer understanding among the chairs of the performance expectations by which their departments were being assessed, and to a belief that a rational a fair process was used to determine faculty line allocations.
Strategically Managing Allocation of Faculty Lines and Types Across Departments and Colleges – A Risk Based Model by Graham Glynn is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Strategically Managing Allocation of Faculty Lines and Types Across Departments and Colleges – A Risk Based Model by Graham Glynn is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Categories: Chief Academic Affairs Office Staff, Dean’s Office Staff (Deans, Executive Deans, Associate/Assistant Deans, etc.), Department Chair Office Staff (Chairs, Assistant Chairs, Program Directors, etc.), Featured, President’s Office Staff
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