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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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slide1

Identifying Cost Efficient Practices

In Administrative Services

In UK Universities

Prof. Emmanuel Thanassoulis

Aston University

e.thanassoulis@aston.ac.uk

slide2

Aims of the Study

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

slide4

The Input-Output Framework

Support

Services

To Students

Labour

Support

Services

To Staff

Central

Administration

Liaisons with

Other Bodies

Capital

Support to

Technology

Transfer

slide5

INPUTS

OUTPUTS

  • Total Income from
  • Students
  • Total Costs
  • (Administrative
  • Staff Costs + Other
  • Operating Expenses)
  • Total Staff Costs
  • Technology Transfer

The Input-Output Set

slide6

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.

slide7

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)

slide8

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

slide9

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.

slide10

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.

slide11

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.
slide12

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.

slide13

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 

slide14

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.
slide15

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.
slide16

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

slide17

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

slide18

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.
slide19

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

slide20

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.
slide21

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

slide22

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.
slide23

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.
slide24

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.
slide25

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.
slide26

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.
slide27

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.
slide28

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

slide29

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.
slide30

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.
slide31

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.
slide32

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.

slide33

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.