ASSESSMENT OF TECHNICAL EFFICIENCY OF INDIAN B-SCHOOLS – A COMPARISON BETWEEN THE CROSS-SECTIONAL AND TIME-SERIES ANALY

1. ASSESSMENT OF TECHNICAL EFFICIENCY OF INDIAN B-SCHOOLS – A COMPARISON BETWEEN THE CROSS-SECTIONAL AND TIME-SERIES ANALYSIS Presented by Gokulananda Patel Birla Institute of Management Technology India gn.patel@bimtech.ac.in

2. Brief Introduction • PRE Liberalization - the government had a very a vital role in the development of Indian economy. • Most of the industries were in the hands Governments, Only a handful was open for the private sector. And as these private sector units were operating in a monopolistic environment they were still profitable despite producing low quality products and providing poor customer’s service.

3. Brief Introduction • POST Liberalization - Economy open for private and foreign companies. • Entry of multinationals - large operations, optimum capacity utilization, accompanied with massive advertisement and effective sales promotions, backed by quality product and along with good customer relationship. • This has compelled the Indian corporate sector to give more stress on quality of products, reduction in the cost of production, provision for better after sales service, adoption of modern technology, to engage efficient human resources and providing a fair rate of return to the investors • In this background the responsibilities of management institutes have gone up manifold and has necessitated the efficiency measurement of these schools.

4. Some Indian magazines doing the B-School Rankings • Outlook : Cfore • Business World : COSMODE • Indian Management : IMRB The parameters covered are • Intellectual capital, Infrastructure, Admission, Placements, Research and Publications etc. • Why these rankings are important • Enhances the prestige of individual MBA programmes • Influences the starting salary of students (placement performance) • The Stakeholders may use these ranking to choose the School.

5. Ranking of B-Schools may differ depending on the target audience • The Ranking criterion of Students may be different from the criterions of Employers. • For Program Administrator all the criteria as considered by Students and Employers may be important.

6. Ranking of B-Schools may differ depending on the target audience (Contd.) • students may be more interested in the placement performance of the school, the average salary at the initial placement etc. • The employers may be more interested in the intellectual capital, infrastructure facilities etc.

7. Comparison of Outlook –Cfore Survey & Business World – COSMODE Survey

8. Interpretation from Comparison • On comparison of the above table above it is clear that • both the survey uses different set of parameters and assign different weights to them. • The weight given to placement performance and industry interface by Cfore survey is 38% whereas the weight given to these parameters by COSMODE is 30%. • Similarly the sub parameters considered in Intellectual Capital & Faculty and extra curricular activities in C fore survey is same as the sub parameters considered in Faculty, Research & Consultancy, MDP and other programmes in COSMODE, • the weightage given by Cfore survey to this parameter is 20% whereas the weightage given by COSMODE is 25%.

9. Interpretation from Comparison (Contd.) • The methodology of converting the raw data into scores on various parameters across these two surveys also varies. • The COSMODE survey uses both absolute and relative scale to assign the scores based on the nature of the parameters. Wherever the absolute score is used it is based on the slabs or intervals, the relative score is assigned based on the comparison with the highest in the category. • In the Cfore the marks are allotted in a particular category by normalizing against the topper in that category. • The number of institute participating in these two surveys also varies, the C fore survey invited 950 B-School out of them 236 participated, whereas for the COSMODE 384 B-Schools were given with questionnaire out of which 130 B-Schools submitted the completely filled in questionnaire and they ranked 100 B-schools.

10. Comparison of Ranks

11. Sub-parameters& their Weightages for CFORE Survey

12. Sub-parameters& their Weightages for CFORE Survey contd…

13. Objective Ranking from Students, Employers & Programme Administrators Perspective • The cfore ranking - uses surveys of students, employers and programme administrators to measure their combined perceptions on the quality of MBA programmes. we develop objective ranking of B-Schools that address the interest of students, employers and MBA programme administrators separately. • Student- “PP”, “SAL”, and “FEE (29 schools lost rank- 5 have lost within 5 positions “ICFAI Hyderabad” lost 20 positions. Three schools maintained rank) • Employer -“IC”, “IF”, “ECA”, “IL”, “RS” (23 schools lost their rank out of which 19 lost within 5 position ) • Programme Administrators –All Parameters (44 schools lost their rank) So we want an alternative ranking method which is capable to handle complexities involved in ranking the B-Schools and demands for a methodology which is mathematically robust.

14. Berlin Principles International Rankings Expert Group (IREG)-2006- Highlights • Rankings -effective techniques of assessing higher education inputs processes and output. • Ranking process should recognize the diversity among institutions and take the divergent goals and missions of the institutions into consideration. • Authenticity of databases. • The process should take into account the linguistic, cultural, economic and historical contexts of the educational systems being ranked. • There should be transparency about the methodology used for creating the rankings. • Weights should be assigned to various indicators

15. Choosing The Suitable Technique • Analytical Hierarchy Process (AHP) • Bayesian Latent Variable Model • Data Envelopment Analysis (DEA)

16. Choosing The Suitable Technique contd… • AHP helps capture both subjective and objective evaluation measures, providing a useful mechanism for checking the consistency of the evaluation measures and alternatives suggested by the team thus reducing bias in decision-making. • Though AHP has many advantages ,it has some limitations which make the method difficult to apply to B-school ranking. • The B-School ranking problem involves large number units (B-Schools) and wide range of parameters; such situation may make the pair wise comparison difficult. • AHP assumes linear independence of criteria and alternatives. If there is dependence among the criteria, Analytic Network Process (ANP) is more appropriate yet ANP requires far more comparisons, which may be formidable in practical decision environment (Ozden & Birsen, 2005). • The other drawbacks can be- the highly subjective nature of preference weights , Problems with inconsistencies in preferences between objectives sometimes arise(Qureshi & Harrison, 2003).

17. Choosing The Suitable Technique contd… • Not many applications of Bayesian latent variable model are found on the body of literature. • The model works in two steps; first it calculates the relative importance of parameters using information embedded in the data then simultaneously determines the degree of uncertainty that surrounds the ranks. • The method considers the variability present in the data and according adjustments is made. If an institution is performing well in all the parameters then clearly it is ranked above all other institute. • Though the method has some merits, but application of such method makes it difficult to distinguish the performance of public and private institutions specially in Indian context where public institutions receive lot of Government funding and expected do very well in certain inputs like infrastructure, faculty strength etc.

18. Choosing The Suitable Technique contd… • Data Envelopment Analysis on the other hand is non-parametric method based on the application of a mathematical technique called linear programming. It has been successfully employed for assessing the relative importance of set of firms, which use a variety of identical inputs to produce a variety of identical outputs. Unlike statistical methods of performance analysis, DEA is non-parametric in the sense that it does not require an assumption of functional form relating inputs to outputs. • We Choose Data Envelopment Analysis for ranking the Indian B-schools.

19. Research Objectives • The objectives of present study focuses on ranking of some B-schools of India based on their efficiency scores, • Find out bench marking institutions and discusses improvement areas for inefficient institutions. • The study is also extended by considering the performance of the B-schools across the year. • A sensitivity analysis is done to know the robustness of the model.

20. Data Set: • For our study we have considered the surveys conducted by one of the popular Indian magazine Outlook. • The data is collected for two consecutive years i.e. 2004 and 2005. • These surveys are conducted to rank top B-Schools in India based on their performance for the corresponding years. • The data is compiled from various sources (outlook,2004; Bschool Directory,2005; Business School Directory, 2005, www.bschools.com). We have top twenty eight Indian B-schools for analysis.

21. Data Classification and Reversal for DEA Applications • For our DEA analysis the data is classified into two categories viz. inputs and outputs. The criteria of selection of inputs and outputs are quite subjective; there is no specific rule for determining the procedure for selection of inputs and outputs (Ramanathan, 2001). The parameters used for the analysis is shown below

22. Input – Output Classification

23. Data Reversal • For applying the DEA we have reversed two of the inputs “IC” and “IF”. • The total score for Intellectual capital (IC) and infrastructure and facilities (IF) are 250 and 200 respectively. • These scores are not directly taken for DEA analysis as higher score of IC and IF means they have developed more infrastructure, facilities and intellectual capital, which is desirable. • If we directly use the score in the model higher value will be reflected as usage of more input for producing the desired output, which is contradictory. • So for the DEA analysis the complement of the score from the total is used i.e. if an institute scored 230 out of 250 in an input variable then input value is taken as 20.

24. Analysis and Results • The general output oriented BCC DEA model is used to solve the problem and get the efficiency score. • The result of DEA analysis is shown in Table –I. The 1st column of the Table-I shows the rank as assigned by the Magazine, the 2nd column shows the efficiency score as calculated from BCC model for the year 2004 and 3rd column for the year 2005. The 4th column is new rank assigned to the B-Schools based on the efficiency score for 2004 and 5th column for the new rank for the year 2005. The 6th and 7th column shows the deviation in conventional ranking and the DEA ranking for 2004 and 2005 respectively.

25. Table-I: Results of BCC -Output orientated DEA Model

26. Results Contd…

27. Discussion • The results show that top six Indian B-schools are retaining their positions. • There is a improvement in the mean efficiency score of the institute over the year. • The high value of efficiency score is obtained as only very top B-schools are considered for the analysis. • It is also interesting to see the last two columns that the position of top five schools is not changing over the year. • DMU1 which is one of the top B-School in India (IIM-Ahmedabad) is retaining its position in all rankings. • The highest loser in the year 2004 on technical efficiency score is DMU11 which lost seventeen position and highest gainer being DMU24 and DMU26 which gained fourteen positions. • Similarly the last column of the Table-I shows the lose and gain of the B-Schools for the year 2005.

28. Sensitivity Analysis • DEA is an extreme point technique because the efficiency frontier is formed by the actual performance of best-performing DMUs. • A direct consequence of this aspect is that errors in measurement can affect the DEA result significantly. • So according to DEA technique, it is possible for a B-School to become efficient if it achieves exceptionally better results in terms of one output but performs below average in other outputs.

29. Sensitivity Analysis The sensitivity of DEA efficiency can be verified by checking whether the efficiency of a DMU is affected appreciably • If only one input or output is omitted from DEA analysis. • Dropping one efficient DMU at a time from DEA analysis. • For our study the robustness test of the DEA results obtained is done in two ways: • Initially the input “Intellectual Capital” is dropped from the analysis and technical efficiency of DMUs is calculated, then input “fee” is dropped, similarly the outputs “Industry Interface” and “Placement Performance” is dropped one by one. • At the second level the efficient units “DMU1”, “DMU12is dropped one by one and technical efficiency is calculated.

30. Table-II: Sensitivity Analysis Report (Output Oriented DEA, Scale Assumption: VRS)

31. Table-II: Sensitivity Analysis Report (Output Oriented DEA, Scale Assumption: VRS)

32. Discussion • It is observed from the table above that when the input IC is dropped from the analysis there is no change in the technical score. • When the input “Fee” is dropped from the analysis then there is change in efficiency scores two DMUs viz. DMU5 and DMU14 is becoming inefficient. • Dropping the efficient DMUs from the analysis is not making the efficient units inefficient one. The analysis shows the robustness of the model used.

33. Time-Series Analysis • A time series are the values of a function sampled at different points in time • In this section we have observed the DMUs over multiple time periods to find the changes in efficiency over time. In such a setting, it is possible to perform DEA over time by using a moving average analogue, where a DMU in each different period is treated as if it were a "different" DMU. Specifically, a DMU's performance in a particular period is contrasted with its performance in other periods in addition to the performance of the other DMUs (Cooper et al.).

34. Results

35. Results

36. Discussion • The above table reflects the stability of technical efficiency score over a period of time. It is observed that the technical efficiency score of inefficient units has decreased in period -2, but is maximum in period -3. The performance of DMU14 and DMU16 has come down over the period and is becoming relatively inefficient over the period

37. Conclsion • As the management education characterizes multi-input and multi-output system. Data Envelopment Analysis (DEA), with its ability to handle multiple inputs and multiple outputs has been used in this paper to rank the Indian B-Schools based on their technical efficiency score. • The ranking is done using BCC model and the results are compared with conventional ranking done by popular Indian magazines. The comparison shows that ranking using DEA-VRS model differs significantly from the conventional ranking.

38. Coclusion • The sensitivity analysis done shows that there is no significant change in the efficiency score of DMUs when an input or output is dropped from the DEA analysis. • The dropping of efficient DMUs from the analysis also shows the same fact. This shows the robustness of the model. • The time series analysis done over three periods of time shows the stability of technical efficiency score over a period of time. It is observed that the technical efficiency score of inefficient units has decreased in period -2. The analysis has shown the performance of schools has improved over period of time. • The methodology suggested in the paper can provide useful information by identifying clusters of DMUs performing better in certain contexts. This technique allows the researcher to investigate why and how they are able to perform better.

39. References: • Banker R.D, Charnes.A , Cooper W.W (1984) “ Some models for estimating technical & scale efficiencies in Data Envelopment Analysis” , Management Science,30 (1984), 1078-1092 • B – School Directory 2005 “Largest Listing of B-Schools” by Business India. •  Business School Directory – 2005 by Dalal Street. • Charnes.A , Cooper W.W & Rhodes.E, “ Measuring efficiency of decision making units” , European journal of Operational Research 2 (1978) ,429-444 • Juran, J.M. and Gryna, F.M.Jr (Eds), (1988), Juran’s Quality Control Handbook, 4th ed., McGraw-Hill, New York, NY. • Natarajan, R., 2003, “Quality and Accreditation in Technical & Management Education”, Productivity, Vol .44 No.2, July-September. • Parasuraman, A., Zeithaml, V. A. and Berry, L.L. (1985), “A Conceptual Model of Service Quality and its Implication for Future Research”, Journal of Marketing, Vol.49 (Fall), pp. 41-50. •  Peters, T.J. and Waterman, R.H. (1982), In Search of Excellence, Harper and Row, New York, NY. • Ramanathan.R “ A Data Envelopment Analysis of comparative performance of schools in Netherland” Opsearch Vol. 38 No.2 –2001,Page no. 160-182 •  Sreekumar, G.N.Patel, (2005), “ Measuring the Relative Efficiency of Some Indian MBA Programmes- A DEA Approach”, Business Perspective, Vol.7, No.2, July-Dec 2005,pp-47-59 •  Sreekumar, G.Patel, (2007), “Comparative Analysis of B-school Rankings and an Alternate Ranking Method”, International Journal of Operations and Quantitative Management, Vol 13, No.1, March, 2007, PP-33-46 •  Outlook –September 27,2004 •  Outlook –September ,2005 •  William W. Cooper, Lawrence M. Seiford and Joe Zhu, Data Envelopment Analysis History, Models and Interpretations • www.indiabschools.com

40. Thank You