A Look Under the Hood

1. Virtual U A Look Under the Hood William F. Massy The Jackson Hole Higher Education Group, Inc., and the National Center for Postsecondary Improvement, Stanford University Stanford, CANovember 3, 2000

2. What We’ll Cover • Architecture • Student sims • Faculty sims • Response functions • The 12 model groups

3. Data Sources • Data were collected by the Institute for Research on Higher Education, University of Pennsylvania • Secondary source data (including IPEDS, College Board, NELS): e.g., • faculty & student numbers, diversity, salaries, financial profiles, financial aid, student segment data. • “Scavenger hunt”: e.g., • curricular requirements, graduation rates, athletic costs & revenues, facilities costs, faculty time profiles.

4. Resource AllocationProcedure for Balancing Priorities • Simulates the university’s budget office. • Presidential inputs: • targets; priorities; upper and lower limits. • Procedure • Minimize the weighted sum of the squared differencesbetween the result and the target, while observing the limits to the extent possible. • The results are shown in the right-hand column. • Presidents can change the inputs and ask the budget office to repeat the procedure.

5. Resource AllocationStage 1.Revenue and Expenditure What gets balanced: • Growth rates of revenue (gR) • tuition, financial aid, endowment spending, indirect costs on research (given admission targets) • Growth rates of expense (gE) • faculty and staff salaries, real operating budget growth, transfer to plant • The surplus or deficit Reality check: (1+gR) Revt–1 – (1+gE) Expt–1 = Surplus (deficit)

6. Resource AllocationStage 2.Allocations What gets balanced: • Growth rates of expense for individual functions and offices (gi) • faculty expenses (excluding the salary increases covered in Stage 1) • non-faculty departmental expense, libraries, information technology, athletics, student life, development office, administration, O&M, enrollment management Reality check: Sumi (1+gi) Expi, t– 1 = (Real budget growth) Expt– 1

7. Resource AllocationStage 3.Faculty Hiring What gets balanced: • Departmental faculty budgets • faculty budgetd (excluding the salary increases covered in Stage 1) • new faculty hiresd = faculty budgetd– cost of continuing facultyd • The president can override the budget. Reality check: Sumd Faculty budgetsd= total faculty expense

8. Enrollment Management • Student application and enrollment decisions are simulated for seven student segments. • Decisions depend on institutional characteristics, tuition, and financial aid policies. • Admission decisions allocate the target intake according to institutional priorities. • academic, extracurricular, and athletic performance; gender; minority status • Procedure takes account of prior yield rates.

9. Student Segmentation Scheme Extracurricular index (v2) Membership 5.5 4.0 0.0 10.0 1. Blue Chip 5.8% 2 Academic 2. Scholar 9.2% 1 7.0 index 3. ExtraCurr. 5.8% 4 5.5 5 (v1) 4. Athlete 8.3% 6 3 5. Balanced 25.7% 4.0 6. Average 30.5% 7 7. Stretch 14.7% 0.0 Gender, minority status, and athletic ability are included in the database. Based on the National Education Longitudinal Survey (NELS)

10. Simulated Students • Program level and year in program • Gender/ethnic group • Talent indices • academic, extracurricular, athletics (L1 only) • Financial need status (undergraduate only) • Major • undergrads decide after year 1 • Courses passed • Satisfaction indices • academic, extracurricular, athletics (L1 only)

11. Student Model • Students take courses; graduate when they’ve accumulated enough credits. • Separate requirements for general education & major. • Courses may be full; denial may delay progress. • Failure probability = r{student talent, ed qualty} • Student acad satisfaction = r{ed quality, libraries & IT, course denials, stuLife$, athletics record} • Pr(dropout) = r{student satisfaction (acad, x-c, ath), course failures, diversity index} • Students(t) = students (t –1)– graduates – dropouts + new matriculants

12. Response Driver variable Response Driver variable Response Functions Other features Logistic (“S”) curve 1. Multiple variables: apply response functions first, then take the weighted sum. 2. Latency:makes the effect evolve gradually;y(t) = l y(t–1) + (1–l) x(t),where x(t) is the weighted sum. Dual logistic curve

13. Faculty Hiring • Number of hires & departmental priorities are determined by Resource Allocation. • President sets the priority sliders: • new blood (ass’t prof); leadership (full prof); cost containment (low salary); gender/ethnic; adjunct; talent profile. • Model predicts market salaries, then uses linear programming to allocate the available hires according to priorities.

14. Name, department Rank, age, time at institution, off-duty term Talent indices teaching, research, scholarship (not adjunct) Salary Performance teaching, research, scholarship (not adjunct) Sponsored research current projects; proposals outstanding Satisfaction Simulated Faculty

15. Faculty Model • Faculty teach, do research and scholarship. • Teaching perf = r{talents, time distrib, acad support} • Scholarship perf = r{above, with different weights} • Research perf = r{above, plus direct sponsRes$} • Faculty satisfaction = r{salary, teaching load, diversity index, perfVsTalent, discTimeStrain, dept acad standing, promotion difficulty [ass’t]} • Pr(departure) = r{satisfaction} • Pr(promotion) = r{performance, prom. difficulty} • Faculty(t) = faculty(t –1) – departures + hires

16. Teaching Model • Course demand: student sign-ups… • depend on remaining requirements: by field, prerequisites, and target cohort of course • Course Supply: faculty assignments… • depend on course type mix, normal class sizes, normal teaching load; “stretch” factors. • Matching up supply and demand • Students admitted to courses when space is available. • New sections opened when faculty are available to teach; otherwise the course is closed. • Ed. Quality = r{facPerfs, edDevTime, IT, class size}

17. Research Model • Prof’s “quality drivers” = r{res talent,res perf (t–1), dept acad standing (t–1), institutional prestige (t–1)} • Pr[proposal] = r{field, quality drivers, res time, docStuQual, current projects} • Pr[project size] = r{field, quality drivers} • Pr[award] = r{quality drivers, indCostRate} • University overhead recovery = indCostRate *(total research volume)

18. Student Life and Athletics • Student life satisfaction = r{student x-c talent, studentLife$, athletics$} • Student athletics satisfaction = r{student athletics talent, athletics record} • Athletics record = r{NCAA level, athletics$, top athletes, average athletic talent} • Football: for men; Basketball: for women • Changing NCAA level changes spending & talent requirements and revenue expectation.

19. Development and Endowment • Monthly gifts = r{develop$, inst prestige, alumni morale, athletics record} • Designated for current use, endowment, and facilities. • Alumni morale = r{instPrest, surplus(def), athPerf} • Investment return = f{asset allocation} • Expected return is based on finance research. • Asset allocation is set by the president. • Endowment spending = (spending rate) * (smoothed endowment market value) • Spending rate is determined in Resource Allocation. • Smoothing is based on 3 or 5-year moving average.

20. Facilities • Deferred maintenance = r{O&M$, space in use, deferred maintenance (t–1)} • Space need = f{student & faculty numbers by dept, total operating exp} • New space is built to meet need if funds are available. • Failure to meet need hurts performance and morale. • Funds come from Facilities Reserve and new debt. • Facilities Reserve comes from the budget and gifts. • Percent funded by debt and the internal debt limit are presidential policies. • External debt capacity = f{assets & liabilities}