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Identifying Cost Efficient Practices In Administrative Services In UK Universities

Identifying Cost Efficient Practices In Administrative Services In UK Universities. Prof. Emmanuel Thanassoulis Aston University e.thanassoulis@aston.ac.uk. Aims of the Study.

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Identifying Cost Efficient Practices In Administrative Services In UK Universities

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  1. Identifying Cost Efficient Practices In Administrative Services In UK Universities Prof. Emmanuel Thanassoulis Aston University e.thanassoulis@aston.ac.uk

  2. Aims of the Study To identify good quality cost efficient practices in the delivery of Central Administrative Services in UK Universities.

  3. The Unit of Assessment Core CAS

  4. The Input-Output Framework Support Services To Students Labour Support Services To Staff Central Administration Liaisons with Other Bodies Capital Support to Technology Transfer

  5. INPUTS OUTPUTS • Total Income from • Students • Total Costs • (Administrative • Staff Costs + Other • Operating Expenses) • Total Staff Costs • Technology Transfer The Input-Output Set

  6. Benchmark on students / £ Benchmark on grants / £ Ratios Under Multiple Resource and/or Outcome Measures • The two ratios do not lead to the same benchmark operating unit. • A benchmarking methodology taking multiple resource and outcome measures JOINTLY into account is required.

  7. An ‘efficient boundary’which envelops from above the observed research grant and student levels per unit of CAS spend is identified. Thevirtualbenchmark VBM6 offers the top levels of research grants and students when keeping to the mix of research grants to students of CAS6. VBM6 The distance from CAS6 to VBM6 reflects the scope for savings at CAS 6. Data Envelopment Analysis (DEA)

  8. The space of all possible production points in a DEA model is specified as the feasible region of a linear programming model. The distance of a real production point (unit) from the boundary of the space constructed is determined by optimising an objective function on the above linear programming model. An introduction to Data Envelopment Analysis can be found in: E. Thanassoulis (2001) Introduction to the Theory and Application of Data Envelopment Analysis: A foundation text with integrated software. Kluwer Academic Publishers, Boston, Hardbound, ISBN 0-7923-7429-0

  9. TERMINOLOGY Benchmark CAS unitsThese are units that relative to the rest of the CAS units are found to have the lowest level of spend when we control for their mix and absolute levels of student income, non-CAS staff spend and Technology Transfer. Non Benchmark CAS unitThese are CAS units which are not benchmark in the foregoing sense. Benchmark spendThis is the level of spend a CAS unit is estimated would need to have to match the benchmark CAS units when we control for mix and absolute levels of student income, non-CAS staff spend and Technology Transfer.

  10. Three Related Measures of CAS Spend Have Been Modelled: - Aggregate CAS staff and Operating expenditure - CAS staff expenditure Only - CAS OPerating EXpenditure (OPEX) only. In each case we have controlled for mix and absolute levels of student income, non-CAS staff spend and Technology Transfer. The data we have analysed relates to 1999/2000.

  11. The aggregate CAS expenditure model is a more appropriate benchmarking instrument the more mutually substitutable OPEX and CAS staff spend are in delivering CAS. • The separateCAS staff and CAS OPEX models are more appropriate benchmarking instruments the more mutually NON - substitutable OPEX and CAS staff expenses are in securing the CAS deliverables.

  12. CAS Devolved To Academic Departments NO DEVOLVED ADMINIn this model the assumption was made that non-academic staff costs in academic departments ARE NOT part of CAS staff expenditure. DEVOLVED ADMINIn this model the assumption was made that ALL non-academic staff costs in academic departments ARE part of CAS staff expenditure. MEAN DEVOLVED ADMINThe average between the two benchmark spends derived from the two purist models give the mean devolved benchmark spend.

  13. Summary of Benchmarking Computations Models Used No Devolvement Devolvement Mean DevolvementResource being modelledAggregate CAS Staff and OPEX Spend  CAS Staff Spend Only  CAS OPEX Spend Only 

  14. Interpreting Benchmark Spend Percentages • Take as an example the Mean Devolvement Benchmark spend for I36 as Percent of Actual Spend Aggregate CAS CAS Staff CAS OPEX I36 91.790 78.036 94.550 • The 91.79% under Aggregate CAS applies if we assume CAS staff and OPEX spend are for the most part mutually substitutable. In that case the overall CAS expenditure of I36 can reduce by about 8%. • The percentages under CAS Staff and CAS OPEX apply if we assume CAS staff and OPEX spend are for the most part NOT mutually substitutable.

  15. Interpreting Benchmark Spend Percentages • The CAS Staff percentage of 78.036% shows that the unit can save about 22% of actual CAS staff spend relative to its enchmarks on CAS staff. • The CAS OPEX percentage of 94.55% shows the unit can save about 5% of actual OPEX spend relative to its benchmarks on OPEX. • The three estimates of benchmark spend above need to be seen as ‘broad brush’ indications given the caveats on data shortcomings to be made later.

  16. Median = 77% Median = 71% Median = 74% Summary of Benchmarking Computations Aggregate CAS Staff and OPEX Benchmarking Mean Devolved Admin BENCHMARKS Devolved Admin No Devolved Admin Benchmark expenditure as percent of observed expenditure

  17. Aggregate CAS Staff and OPEX Spend 4000000 Scope for 3500000 Savings 3000000 2500000 (£000) 2000000 1500000 Total Benchmark 1000000 Aggregate 500000 CAS Spend 0 No Devolved Admin Devolved Admin Mean Devolved -No Devolved Aggregate CAS Staff and OPEX Spend The sum of benchmark and scope for savings is the observed level of spend. The scope for savings is stated in £m. £780m £737m £693m

  18. Benchmark CAS Units On Aggregate Staff And OPEX Inst No Devol Devolved Admin Admin 1 100 100 2 100 100 3 100 100 4 100 100 5 100 100 6 100 100 7 100 100 8 97.822 100 9 100 100 10 100 98.468 11 100 100 12 100 98.572 13 100 100 15 99.66 100 16100 89.106 17 100 100 18100 82.459 24 100 100 • The benchmark spend as % of the observed spend is shown. Where we have 100% we have a benchmark unit under the model concerned. • Clearly benchmark CAS units are virtually identical under the devolved and not devolved administration models where aggregate spend is concerned.

  19. Median = 66% Median = 69% Median = 63% CAS Staff Spend Benchmarking Mean Devolved Admin BENCHMARKS Devolved Admin No Devolved Admin Benchmark expenditure as percent of observed expenditure

  20. Benchmark CAS units on Staff Spend No Devld Admin Devd Admin HEI 1 93.24 100 4 100 58.55 8 99.58 100 9 97.11 100 10 100 98.27 11 100 81.06 12 100 100 15 86.91 100 17 100 100 22 88.67 100 23 100 84.41 24 59.2 100 25 100 94.38 26 100 75.62 30 95.36 100 34 100 95.58 50 100 82.7 95 100 89.87 • Where we have 100% we have a benchmark unit under the model concerned. • We have a large measure of agreement between the models on benchmarks but also some significant differences (highlighted). • The differences arise mainly where a benchmark unit has a very large part of CAS devolved to academic departments.

  21. CAS OPEX Benchmarking BENCHMARKS Mean Devolved Admin Median = 65% Median = 67% Admin Devolved Admin Median = 62% No Devolved Admin Benchmark expenditure as percent of observed expenditure

  22. Using The Identified Benchmarks • All our estimates of benchmark spend are based on SPECIFIC BENCHMARK units identified to match the activity volumes and mix of the unit whose benchmark spend we wish to estimate. • Special sets of benchmarks are available for all non-benchmark CAS units from each one of the Devolved - No Devolved administration models and for each resource (Aggregate, OPEX or Staff ) modelled. • An illustration of how benchmarks specific to each non-benchmark unit can be useful follows.

  23. Using The Identified Benchmarks • The table below shows the specific benchmarks identified for I36 under the Devolved Admin model, when CAS staff is the resource modelled. Data on each variable is indexed for anonymity so that I36 = 100. • I36 and I30 are post 1992 Universities while I46 is an ‘old’ university. • All three units have about 50% of potential CAS staff spend devolved to academic departments and so we have in effect controlled for CAS staff devolvement.

  24. Using The Identified Benchmarks • Usually, but not always, one or more of the special benchmarks chosen by DEA can be used to see why the non-benchmark unit was found to have scope for efficiency savings. • In the case of I36 its benchmark I46 can play this role. • I46 administers 4 times the volume of Technology transfer of I36, nearly 30% larger non CAS staff spend and 84% of the student income of I36 . • Thus even if we ignore the higher volumes of non CAS staff and Technology Transfer at I46 we would expect its CAS spend to be of the order of 84% of that of I36.

  25. Using The Identified Benchmarks • There may be factors outside the model (such as mix of students administered or the quality of service at I36 being better and costlier than at I46 ) that explain the apparent difference between I36 and I46 on CAS staff spend. However, it is also possible that there is a genuine difference in operating practices between the two that explains in part the lower CAS staff spend at I46 when we control for activity levels. • Comparisons of this type can be made for all non-benchmark institutions on each model used relative to one or more of their specific benchmarks.

  26. Unit 46 0.00% 33.33%33.33% 33.33% Unit 73 Scale effects -1.22% STUDENT INC 9.29% NON CAS STAFF 9.29% TT 82.63% Contracting Two Benchmarks CAS Units Percent CAS staff spend attributable to each activity • For unit 73 diseconomies of scale justify about 1.22% of CAS staff spend. Over 80% of CAS staff spend needs to be attributed to TT activity for unit 73 to justify in full its CAS staff spend. • For unit 46 there are no dis- or economies of scale. Further, it does not specifically need to play up the importance of any one of the three surrogate measures of activity we are using in order to justify its CAS staff spend.

  27. Contrasting Two Benchmarks CAS Units • Both I46 and I73 are collegiate ‘old’ research-intensive universities. It does appear though that in either student or staff volume administration or both I46 may have better practices which would benefit I73 despite I73 itself being a benchmark. • The DEA analysis reveals information of this type which could benefit the benchmark CAS units to adopt the best practices from other benchmark units.

  28. Summary • We have used three surrogate measures of CAS activity: - Student income (£000) - Total non CAS staff costs (£000) - Technology Transfer (£000) (Research grants, other services rendered etc.) • Controlling by means of DEA simultaneously for the three measures above we have benchmarked CAS units in turn on three measures of spend: - Aggregate CAS staff and Operating expenditure - CAS staff expenditure only - CAS OPerating EXpenditure (OPEX) only

  29. Summary • The data we have analysed relates to 1999/2000. • The spend modelled was alternately computed assuming non-academic staff costs in academic departments are and are NOT part of CAS spend. • We have found large measure of agreement in the benchmarks identified for each type of spend modelled, under the two alternate assumptions above on treating non academic staff spend in academic departments. This is less true when CAS staff spend is modelled.

  30. Summary • On balance, using the ‘mean devolved administration’ result is likely to be a better estimate of the relative performance of a unit on each spend modelled. • If we assume CAS OPEX and CAS staff spend are in large measure mutually substitutable then the aggregate CAS and OPEX spend model applies. • Under this model we estimate that the median CAS unit can reduce total spend by some 25%, amounting for the sector to potential savings of some £737m.

  31. Summary • DEA clusters each non-benchmark unit within a small subset of the benchmark units, those most closely matching it on scale size and mix of activities. • We have indicated how such small groups of specially identified units may readily compare their data and generally share operating practices found at benchmark units. • We have also indicated how benchmark units can themselves identify other benchmark units which will offer complementary best practice to their own.

  32. Caveats • Our findings could be biased for a number of reasons: - The surrogate variables we have used (student income, non-CAS staff spend and Technology Transfer income) may not reflect with similar accuracy volumes of CAS activities across HEIs. - We have not reflected in the modelling any variation in quality of service offered by CAS units across HEIs. This part of the project is in the process of being carried out. - Data may not be consistently returned by HEIs as there is latitude in interpreting data headings on HESA returns.

  33. Caveats - We have raised but not resolved the question as to whether CAS staff and CAS OPEX spends are mutually substitutable and if so to what degree. - We have been unable to disentangle staff and OPEX spend on CAS from other non academic staff and OPEX spend in academic departments.

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