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Ranking and Relative Efficiencies of India’s Central Universities through the Lens of DEA

Fullstar Lamin Gayang1★, S. Ravikumar2★ Corresponding author

  1. 1Library and Information Science, North-Eastern Hill University, Umshing Mawkynroh, Shillong, Meghalaya, INDIA.
  2. 2Library and Information Science, Central University of Tamil Nadu, CUTN Bridge, Neelakudy, Tamil Nadu, INDIA.

CORRESPONDENCE

Fullstar Lamin Gayang

Ph.D Candidate, North-Eastern Hill University, Umshing Mawkynroh, Shillong, Meghalaya-793022, INDIA.

fullerlamin@gmail.com

ORCID: 0000-0002-0980-2443

Received: 08-01-2025; Revised: 24-02-2025; Accepted: 18-04-2025.

Volume 2, Issue 1 · pp. 4–10 · PUBLISHED Jan-Apr 2025 · DOI: 10.5530/irc.2.1.2

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ABSTRACT

Aim/Background Universities are the cornerstone of a nation’s scientific and technical advancements. This study aims to evaluate the performance efficiency of India’s ten central institutions of higher learning using Data Envelopment Analysis (DEA). Unlike traditional methods that use fundamental performance ratios, this research adopts a multi-input, multi-output technique to provide an additional instrument for benchmarking and assessing decision-making units (organizations). Materials and Methods We employed the DEA super-efficiency model to compare the relative efficiency of ten central universities using NIRF data from 2020, 2021, and 2022. Input variables included total number of faculty, students, capital and operational expenditure, while output variables comprised graduates (UG, PG, Ph.D.), placements, publications, and citation count from Scopus and Web of Science. Results According to our data analysis, Jamia Millia Islamia University was the most efficient university among the ten institutions studied. The remaining nine universities demonstrated inefficiency to varying degrees, with efficiency scores fluctuating across the three-year period. Discussion The study reveals significant variations in efficiency scores across universities and over time, highlighting the complex interplay of factors influencing university performance. The inclusion or exclusion of specific input variables significantly affects efficiency scores, emphasizing the importance of considering institutional context when interpreting results. Conclusion DEA provides valuable insights for university management and policymakers, offering a multi-criteria approach for evaluating higher education institutions. This alternative analysis provides a new perspective for improving university standards and rankings, particularly as government funding is tied to efficient resource utilization.

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INTRODUCTION

Universities are the cornerstone of a nation's scientific and technical advancements, and they also offer cutting-edge workforce training, which may have an impact on the caliber of scientific research as well as regional innovation. It has become necessary for a number of decision-makers, including the government, commercial organizations, and society, to monitor and assess the efficiency of higher education institutions due to their crucial role in economic development. To improve the efficiency of universities, a performance measuring tool is required to assess their performance. Because of the distinct characteristics of universities, it is difficult to determine how well they perform. First of all, as with any other non-profit, it is hard to assign monetary values to the inputs and outputs. Second, a university generates various outputs (such as graduates and research papers) from multiple inputs (such as professors and facilities). A variety of methodologies have been employed to assess university performance, with the most prevalent being Stochastic Frontier Analysis (SFA), and Data Envelopment Analysis (DEA), SFA is effective at managing data with some ambiguity; nevertheless, it is difficult to use in situations with many inputs and outputs. DEA, on the other hand, has grown in popularity as a performance monitoring tool for non-profit organizations.

In this paper, we study the relative efficiencies of ten central universities in India with data gathered from the National Institutional Ranking Framework (NIRF) through a methodology known as Data Envelopment Analysis (DEA) which is different from the traditional process of ranking universities as employed by various ranking agencies all over the world.

The National Institutional Ranking Framework (NIRF) used the weight and sum approach to the combined scores of indicators in the NIRF scores, which were ranked by the institutes. This approach assumes that all indicators contribute independently to the NIRF score in the specified proportions. However, the Data envelopment analysis method that we will employ in this study is different from the traditional process of ranking universities as it is used to measure the relative efficiency of a group of Decision-Making Units (DMUs), in this case, the central universities of India. The DEA model employs the typical input/output ratio to determine efficiency for a particular unit in terms of a formulation of a fractional linear program. The DEA technique asserts that a DMU is considered inefficient if some other DMUs or some combinations of other DMUs create at least the same amount of output with less of the same resources input and not more of any other resources. This is in line with the economic concept of Pareto optimality. In contrast, a DMU is deemed Pareto efficient if the aforementioned is not feasible. In this particular paper, we make use of the “Super-efficiency” model of DEA to assess the relative efficiencies of ten central universities in India.

Although the system used to rank educational institutions is unquestionably founded on strong principles, it does not provide a more precise understanding of how much resources are utilized in producing outputs. University output is primarily measured in terms of the number of graduates at various levels of undergraduate and postgraduate programs, Ph.D. placements, patents, publications by faculty, and citations in reputed journals meanwhile utilising various input units in the form of capital expenditure utilised for (library, laboratory equipment, engineering workshops, and other expenditure) and operational expenditure (salaries, maintenance, seminars/workshops), funding from projects to produce the said output. This study attempts an effort to evaluate, using Data Envelopment Analysis (DEA), the central university's capability to efficiently utilise input resources (capital, operational expenditure, etc.) to produce output (publications, citations, patents, etc.,).

Conceptual framework: Data Envelopment Analysis (DEA)

DEA is a nonparametric frontier estimation methodology that classifies entities into "efficient" or "performers" versus "inefficient" or "nonperformers." This study uses the super-efficiency (supereff) model of DEA to account for real-time indications of using various input units to produce output efficiently. The technique focuses on real performance, not presuming a specific data distribution. Traditional DEA suffers from tied ranks and discrimination, especially in minimal sample sizes. Andersen and Peterson (1993) introduced the "super-efficiency" approach, eliminating censoring of scores greater than one. This allows researchers to identify and rank effective units in the sample, generating more meaningful correlations and measures of central tendency.

LITERATURE REVIEW

On many facets of NIRF Ranking, some researchers have undertaken studies (Mukherjee, 2016; Kumar et al., 2021; Nassa et al., 2021). The ranking criteria used by NIRF are comparable to those used by global ranking systems such as THE World University and QS ranking (Sheeza et al., 2018), while (Srimathi and Krishnamoorthy, 2020) studied on QS Ranking. In recent years, public organisations, as well as commercial enterprises, have become interested in efficient resource allocation. Organisations worldwide are under increasing pressure to increase their performance efficiency to justify the allocation of limited public finances and to draw in more outside funding. In recent years, it has become evident that education quality is of the utmost importance (Hanushek and Woessmann, 2011; Hanushek et al., 2013). From the standpoint of public policy, the effectiveness with which educational institutions translate inputs (like expenses) onto outputs (like student accomplishment) is likewise significant and has been the focus of much discussion (Johnes, 2020).

Although there is a wealth of literature on assessment methods and well-known discussions on resource allocation and university structure (Roessner, 2000), scholars and administrators still see a significant need for novel methodologies. Research has examined connections between different indicators of universities using statistical approaches thus far (e.g. Teodorescu, 2000; Fairweather, 2002).

In comparative efficiency studies conducted across the globe in the public sector, the Data Envelopment Analysis (DEA) method plays a significant role (Chalos, Cherian, 1995; Odeck 2005). Due to the possibility that DEA results could yield useful data for HEI management, it is being used in the higher education sector. Leitner et al. (2007), Taylor and Harris (2004), McMillan and Datta (1998), Bradley et al. (2006), Nazarko et al. (2008), Johnes and Johnes (1993), Sinuany-Stern et al., (1994) provide some notable examples of DEA use in the field of higher education from around the world. DEA not only makes it possible to identify areas that need improvement, but it also outlines the potential for development in certain areas. Additionally, it enables the resolution of issues pertaining to the positives and negatives of HEIs, the method of funding distribution across HEI organizational units, or the ideal size of these units. One significant benefit of using DEA in higher education settings is that it can evaluate a university's effectiveness from a variety of angles.

Existing studies have highlighted numerous characteristics and indicators that various authorities use to assess universities worldwide. Our extensive review of the literature has presented us with a clear indication that while there have been studies of relative efficiencies of higher education institutions, there exists no study yet to compare the NIRF with an alternative approach such as the DEA to rank Indian central universities. Therefore, this study seeks to explore the possibility of assessing the relative efficiencies of India’s ten central universities.

METHODOLOGY

Data published in NIRF report was used for this study. As part of our methodology, we evaluate the relative efficiencies of ten central universities. Each university's three-year NIRF data sheet was used to get the raw data. i.e. 2020, 2021, and 2022.

Rationale for selection of universities

In India, there are 54 central universities currently. The universities selected for this study managed to consistently appear in NIRF’s top 100 ranking for the last three years, i.e., in (2020, 2021, and 2022). The universities selected for our study also spread across India from North to South and East to West, serving various demographics of the country. The universities are - Banaras Hindu University, Aligarh Muslim University, Jawaharlal Nehru University, University of Delhi, Jamia Millia Islamia University, North-Eastern Hill University, Tezpur University, University of Hyderabad, Pondicherry University, and Visva Bharati University. The central universities selected are homogeneous because they perform the same task and have similar objectives. All the central universities utilise academic and non-academic staff, capital, and operational expenditure for teaching and research purposes to produce output in the form of graduation outcomes, placements, patents, publications, and citations. Our study’s inputs and outputs units are also homogeneous in all ten universities.

Decision-Making Units and Input/Output Variables

Ten central universities formed our DMUs for the study- BHU, JNU, DU, NEHU, TU, JMIU, UoH, AMU, PU, and VBU. We performed Data Envelopment analysis through the “super-efficiency” model using data available from the NIRF datasheet. The input and output units are defined below:

Input variables

Total number of faculty, total number of students, males, females, students from within the state and outside the state, economically backward students, and socially challenged students (SC, ST, and OBC), capital and operational expenditure for the previous three years.

Output variables

Number of graduates (UG, PG), and Ph.D., placements, publication, and citation counts from Scopus and Web of Science.

RESULTS

The Data Envelopment Analysis reveals that Jamia Millia Islamia University (JMIU) is the only efficient university in utilising input units to produce output, with nine other central universities inefficient. Table 1 presents the NIRF and DEA scores for all ten universities across the three-year period. In 2020, JMIU ranked 10th, but in 2021, it ranked 6th and in 2022, it ranked 3rd. This growth year on year is the highest JMIU has achieved to date. The super efficiency model of DEA differentiates between efficient and inefficient DMUs, indicating JMIU's higher efficiency than nine other universities. Table 2 provides a comprehensive comparison of NIRF and DEA rankings, demonstrating the differences between traditional ranking methods and efficiency-based assessments.

Table 1: NIRF and DEA scores.
Sl. No.Name of UniversityNIRF ScoresDEA Scores
202020212022202020212022
1BHU63.1564.0263.210.2182240.217995.761673
2JNU70.1667.9968.4718.122818.2758338.093912
3DU60.157.0958.663.5420083.6587254.363441
4NEHU46.8844.2445.441.0126084.1223813.487248
5TU48.7747.2747.483.3188333.3060852.356104
6JMIU61.0760.7465.91193.661252.7395643.56656
7UOH61.759.7161.719.7843855.3935253.576456
8AMU54.358.9761.431.252151.3935582.134589
9PU45.8244.3644.952.9118084.120653.185679
10VBU46.8442.7640.964.74830450.917622.530772
Table 2: NIRF and DEA rank.
Sl. No.Name of UniversityNIRF RankDEA Rank
202020212022202020212022
1BHU3rd3rd6th3rd3rd3rd
2JNU2nd2nd2nd2nd4th2nd
3DU11th12th13th6th8th4th
4NEHU49th59th66th10th6th6th
5TU39th46th59th7th9th9th
6JMIU10th6th3rd1st1st1st
7UOH6th9th10th4th5th5th
8AMU17th10th11th9th10th10th
9PU58th58th68th8th7th7th
10VBU50th64th98th5th2nd8th

The distribution of efficient and inefficient Decision-Making Units (DMUs) across the three years is illustrated in Figure 1, which shows that only one university (JMIU) maintained consistent efficiency throughout the study period. Figure 2 displays the DEA scores from NIRF 2020 data, clearly highlighting JMIU's exceptional efficiency score of 193.6612 compared to other universities. The efficiency patterns for 2021 and 2022 are presented in Figure 3 and Figure 4 respectively, showing the declining trend in JMIU's efficiency scores over time (from 193.66 in 2020 to 52.73 in 2021, and further to 43.56 in 2022), while still maintaining its position as the most efficient university.

Figure 1: Number of efficient-inefficient DMUs.
Figure 2: DEA score from NIRF 2020.
Figure 3: DEA score from NIRF 2021.
Figure 4: DEA score from NIRF 2022.

LIMITATIONS

We have limited this study to just ten central universities. In our test for efficiency, we have encountered some lapses concerning the outcome of the results as some input variables with zero (0) have been shown to positively increase the efficiency of some universities, which is contradictory by nature. Hence, we have omitted four input variables from NIRF 2020, where zero is found to have a higher positive value to the result. We have also taken an average for NEHU’s input variable of economically backward as data for 2021 and 2020 were missing.

DISCUSSION

Our analysis of the relative efficiencies of central universities demonstrates the variable impact of different input factors on overall efficiency. While Jamia Millia Islamia University (JMIU) demonstrates the highest efficiency, its performance declined between 2020 and 2022. Interestingly, JMIU's efficiency score decreased from 193.66 in 2020 to 52.73 in 2021, and further to 43.56 in 2022 (see Appendix -Table 1 & 2). This decline suggests that even the most efficient university's performance can fluctuate, possibly due to changes in resource allocation, policy shifts, or other internal or external factors.

As shown in Figures 2-4, the efficiency scores varied significantly across the three years, with JMIU consistently outperforming other universities despite its declining efficiency trend. The graphical representation in these figures clearly demonstrates the substantial gap between the most efficient university (JMIU) and the remaining institutions

Further investigation into the specific reasons for this decline could provide valuable insights for JMIU and other institutions. In contrast, other universities showed different patterns, with efficiency scores varying across the three years. This highlights the complex interplay of factors influencing university performance, including institutional characteristics, resource availability, and strategic priorities. The variations in efficiency scores across universities and over time suggest that there is no one-size-fits-all approach to achieving optimal performance. Each university must carefully consider its unique circumstances and challenges to identify the most effective strategies for improvement.

We found that the inclusion or exclusion of specific input variables significantly affects the efficiency scores. For instance, JMIU's high efficiency in 2020 was linked to low spending in "Other Expenses." The addition of this variable dramatically increased JMIU's efficiency score, indicating that even seemingly minor input factors can have a substantial impact on overall efficiency. Similarly, JNU's efficiency was influenced by the number of students from "Economically Backward" regions. This finding highlights the importance of considering equity and access in evaluating university performance. Universities that effectively support students from disadvantaged backgrounds may exhibit higher efficiency due to the positive impact of diversity on learning outcomes and research productivity. These findings underscore the sensitivity of DEA results to the selection of input and output variables and the importance of considering the specific context of each university when interpreting the results. Researchers and policymakers should exercise caution when comparing efficiency scores across institutions, ensuring that they account for differences in institutional missions, student populations, and resource constraints.

Furthermore, our analysis indicates a strong correlation between research publication productivity and citation impact, which in turn influences university rankings. This finding aligns with previous research that has emphasized the importance of research excellence in enhancing university reputation and attracting funding. The contribution of the top 10% most productive authors significantly boosts publication output, demonstrating that high research output is a key driver of efficiency. This suggests that universities that prioritize and support their most productive researchers are likely to achieve higher levels of overall efficiency. However, it is also important to recognize the value of all faculty members and to create a supportive environment for research at all levels.

CONCLUSION

This study used Data Envelopment Analysis to evaluate the relative efficiencies of ten central universities in India. Our findings indicate that Jamia Millia Islamia University was the most efficient university. JMIU's consistent efficiency over the past three years, and its high rankings in both national (NIRF) and international rankings (Times Higher Education), highlights its commitment to academic excellence and its ability to effectively translate inputs into outputs. The university's success can be attributed to a variety of factors, including its strong faculty, diverse student body, and focus on research and innovation. However, nine other universities demonstrated inefficiency, with Tezpur University and Aligarh Muslim University being the least efficient. These universities may benefit from a closer examination of their resource allocation strategies, academic programs, and research activities to identify areas for improvement.

The efficiency scores reveal important policy implications for higher education in India. Low-efficiency institutions may suffer from inefficient resource allocation, inadequate infrastructure, or a lack of focus on research and innovation. These institutions should strive to optimize their operations, enhance their academic offerings, and create a more supportive environment for faculty and students. Efficient institutions, on the other hand, effectively leverage inputs to generate substantial outputs, demonstrating the importance of strategic planning, effective management, and a commitment to quality. Specifically, our study highlights that financial resources and research outputs significantly impact a university’s relative efficiency. Universities with adequate funding and a strong research focus tend to achieve higher levels of overall performance.

DEA provides valuable insights for university management, aiding in understanding university rankings, faculty productivity, and the effectiveness of resource allocation. This study demonstrates the value of DEA as a tool for evaluating and improving performance in the higher education sector. The multi-criteria approach offers advantages for authorities managing public funds and for the universities being evaluated. By providing a comprehensive assessment of university performance, DEA can help policymakers make informed decisions about resource allocation and identify areas where targeted interventions are needed. DEA results can guide HEIs in optimizing their operations and resource allocation, enhancing their competitiveness, and contributing to the overall development of the higher education system. This alternative analysis of university efficiency provides a new perspective for improving the standard and ranking of universities, especially as government funding for these institutions is tied to efficient resource utilization. By promoting greater efficiency and accountability, this comparative efficiency study can drive improvements in research and education quality, optimize the allocation of public funds, and enhance HEI management.

While this study has some limitations, such as the focus on a limited number of central universities and the reliance on NIRF data, it reveals significant differences in the efficiencies of the ten central universities. Several universities have the potential for efficiency improvement by adopting best practices, investing in infrastructure, and strengthening their research capabilities. The DEA results should be interpreted cautiously, with a thorough understanding of the data and the context. Future research could expand the scope of the analysis to include a larger sample of universities, incorporate additional input and output variables, and explore the factors that contribute to efficiency differences across institutions.

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Gayang, F. L., & Ravikumar, S. (2025). Ranking and Relative Efficiencies of India’s Central Universities through the Lens of DEA. Information Research Communications, 2(1), 4–10. https://doi.org/10.5530/irc.2.1.2