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Establishing
New Universities in India
An Evidence-Based Suitability Analysis
Arunava Dey (Research Oficer)
Dr. Shashank Shah (Senior Specialist)
December 2024
Working Paper *This is a working paper, subject to further revisions.
** The authors are working in NITI Aayog, Government of India. Establishing New Universities in India: An Evidence-based Suitability Analysis i
Table of Contents
1. Introduction��1
2. Objectives of the Study��1
3. Methodology��2
3.1. Data Analysis��2
3.2. Spatial Analysis��2
3.2.1. Spatial Analysis in the field of Regional Development��2
3.2.2. Spatial Analysis for Higher Education Infrastructure Planning��3
4. Data��4
4.1. All India Survey on Higher Education (AISHE)�� 4
4.2. Census of India��4
4.3. Data Limitations��4
5. Key Findings��5
5.1. Higher Education Institutions in India��5
5.1.1. State-level Distribution��6
5.1.2. District-level Distribution��9
5.1.3. Remote Regions��12
5.1.4. Aspirational Districts��13
5.1.5. Temporal Changes��14
5.2. Socio-demographic Characteristics��17
5.2.1. Number of Universities and Colleges per lakh population��17
5.2.2. Gross Enrolment Ratio (GER) and Gender Parity Index (GPI) ��18
6. Recommendations using Spatial Analysis��21
6.1. District-level Recommendations��21
6.1.1. Capital Cities of States/ UTs without a University��21
6.1.2. Districts without universities but having more than 50 colleges��21
6.2. Region-level Recommendations��21
6.3. State-level Recommendations��27
7. Way Forward��29
7.1 Special attention for improving Gender Parity Index��29
7.2. Tribal/ Forest/ Hilly Districts for Specialized Universities��30
7.3. Technical University/ Vocational Training Institutes in
Industrial Regions��31
8. Conclusion��32
9. References and Bibliography��33
10. Reports and Portals��34 Establishing New Universities in India: An Evidence-Based Suitability Analysis ii
List of Annexures
Annexure I: List of Districts in remote regions# with the number
of universities and colleges��35
Annexure II: List of Aspirational Districts with the number of
universities and colleges��40
Annexure III: List of UTs without any university�� 44
Annexure IV: Districts without any universities but having
more than 50 colleges��45
Annexure V: Districts as identified in the Region-level
Recommendations��48
Annexure VI: Composite score for States for State-level
Recommendations��51 Establishing New Universities in India: An Evidence-based Suitability Analysis iii
List of Figures
Figure 1: Types of Universities��5
Figure 2: Location of Universities��6
Figure 3: Number of Universities (State)��7
Figure 4: Number of Colleges (State)��8
Figure 5: Distribution of Universities by Districts��9
Figure 6: Distribution of Colleges by Districts��9
Figure 7: Number of Universities (Districts)��10
Figure 8: Number of Colleges (Districts)��11
Figure 9: Number of Universities in Remote Regions��12
Figure 10: Number of Universities in Aspirational Districts��13
Figure 11: Number of Major Universities by Type (2015-22)��14
Figure 12: Location of Universities by Type��15
Figure 13: Districts without any University��16
Figure 14: Number of Universities per lakh population (18-23 years)��17
Figure 15: Number of Colleges per lakh population (18-23 years)��18
Figure 16: State-wise Gross Enrolment Ratio��19
Figure 17: State-wise Gender Parity Index��20
Figure 18: Districts without any Universities, having more than 50 Colleges��22
Figure 19: Location of current Universities��23
Figure 20: District Connectivity Histogram��25
Figure 21: Moran Scatter Plot��25
Figure 22: Local Spatial Autocorrelation (Moran’s I)��26
Figure 23: States requiring more Universities (as per the Composite Score)��28
Figure 24: Comparison of GPI in Higher Education and Sex Ratio��30
Figure 25: Location of Technical Universities and Institutes of
National Importance��31 Establishing New Universities in India: An Evidence-based Suitability Analysis 1
1. INTRODUCTION
India became the world’s most populous country in 2023. It is home to more than one-
sixth of the global population. In 2024, the median age of an Indian is 28.4 years, much
lower than most of the other populous countries. This has made India one of the young-
est countries in an ageing world leading to a window of opportunity in terms of ‘demo-
graphic dividend’. On multiple occasions, Hon’ble Prime Minister Shri Narendra Modi has
highlighted the fact that ‘our demographic dividend is our biggest strength’.
The literacy rate in India has been growing consistently, reaching 74.04% in 2011 (82.14%
for males and 65.46% for females). Although the Gross Enrolment Ratio (GER) for Ele-
mentary School Education in India was about 100%, the GER for Higher Education (18-23
years of age group) was 28.4% in 2021-22. This has created a unique demand-and-sup-
ply imbalance in India as less than one-third of the eligible population is pursuing higher
education when compared to educational enrolment at primary school levels. Moreover,
the Government of India has set an ambitious target of achieving GER of 50% in higher
education by 2035. Official projections estimate that this would increase the number
of students in the Indian higher education ecosystem to approximately 9 crores, effec-
tively doubling the student enrolment of nearly 4.33 crore in 2021-22. To accommodate
this significant increase in number of students pursuing higher education in India, about
double the number of existing universities may be needed.
2. OBJECTIVES OF THE STUDY
The location of the existing universities is not evenly distributed over space; rather it is
highly clustered in and around the major metro cities in India. Therefore, there is a need
to study the current pattern and identify suitable locations to make higher education
more accessible across regions, especially in the hinterland.
Against this backdrop, this paper aims to propose an evidence-based policy framework
in India to identify the most suitable locations for setting up new universities in India
using data analysis and spatial intelligence.
The objectives of the study are:
i. To analyse the geographical location and spatial concentration of universities, and
ii. To recommend suitable locations for setting up new universities in the country. JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 2
3. METHODOLOGY
This paper uses the following techniques to achieve the above mentioned
objectives.
3.1. Data Analysis
Data analysis is a process of inspecting, cleansing, transforming, and modelling
data with the goal of discovering useful information, deriving conclusions, and
supporting decision-making. This approach has multiple facets and approaches,
encompassing diverse techniques under a variety of names, and is used in different
business, science, and social science domains. In this paper, data analysis methods
have been used primarily to analyse the existing coverage of higher education in
India. In addition, a weighted composite score has been derived for state-level rec-
ommendations using data analysis.
3.2. Spatial Analysis
Spatial analysis offers a set of techniques to analyse spatial data and comprehend
relationships, patterns, and trends in a geographic context. In other words, spatial
analysis is the process of extracting or creating new information about a set of
geographic features to examine, assess, evaluate, analyse, or model data with spa-
tial reference within a geographic area. According to De Smith et al. (2007), spa-
tial analysis involves the examination of geographic data to uncover patterns, rela-
tionships, and trends. It provides insights into the spatial distribution of resources,
population, economic activities, and infrastructure within a region. It entails the
analysis of data with explicit geographic or spatial attributes for understanding
the spatial distribution and characteristics of various phenomena. This can be car-
ried out using various techniques with the aid of statistics and Geographical Infor-
mation Systems (GIS). GIS is a technological field that incorporates geographical
features with tabular data to map, analyse, and assess real-world problems.
3.2.1. Spatial Analysis in the field of Regional Development
Spatial analysis plays a crucial role in regional development by providing
valuable information for decision-making, planning, and policy formula-
tion. Site selection and land use planning are key applications of spatial
analysis in regional development. In their book “Geographic Information
Science & Systems,” Chang (2019) discuss how spatial analytic techniques
may be used to identify the best places for multiple diverse activities such
as infrastructure development, industrial zones, commercial centres, and
residential areas.
Spatial analysis has been extensively used in several regional and infra-
structure development projects by the Government of India. For instance,
the Ministry of Housing and Urban Affairs (MoHUA) has used geospatial
analysis for the Smart Cities Mission analysis to evaluate the existing in- Establishing New Universities in India: An Evidence-based Suitability Analysis 3
frastructure, identify areas for improvement, and plan for future growth.
MoHUA and the Ministry of Road Transport and Highways (MoRTH) have
used spatial analysis in formulating the National Urban Transport Policy
(NUTP) by analysing land use patterns, population density, and travel de-
mand. The National Wetlands Conservation Programme (NWCP), a flag-
ship scheme by the Ministry of Environment, Forests & Climate Change
(MoEFCC) has used geospatial data to identify wetland areas, assess their
ecological significance, and prioritize conservation efforts. Geospatial
analysis also plays a crucial role in disaster management and risk assess-
ment at the National Disaster Management Authority (NDMA) under the
Ministry of Home Affairs (MHA).
3.2.2. Spatial Analysis for Higher Education Infrastructure Planning
There is a significant opportunity for the Ministry of Education (MoE) to
use this technique in planning and creating higher education infrastruc-
ture. By integrating spatial planning principles into regional development
processes, policymakers can promote the equitable distribution of univer-
sities and colleges across different regions, thereby facilitating equal ac-
cess to education for all individuals. The incorporation of spatial planning
facilitates strategic decision-making regarding the location and design of
university campus buildings and facilities and can ensure their convenient
accessibility for both students and faculty members.
The Institutional Development Plan (IDP) guidelines formulated by the
Expert Committee under the University Grants Commission have also
encouraged the use of Geographic Information System (GIS) and spatial
data for physical infrastructure development in Indian Higher Education.
The NEP 2020 emphasizes the importance of ‘equity and inclusion in High-
er Education’. It has accorded top priority to providing quality higher edu-
cation opportunities to all individuals. To achieve this goal, the integration
of a Spatial Decision Support System (SDSS) for the overall planning of
HEIs becomes essential. SDSS combines spatial data, analytical methods,
and decision-making processes to support complex spatial decision-mak-
ing tasks. It serves as a system that integrates geospatial data, models,
and decision-support tools, to address intricate spatial problems. More-
over, the interactive nature of SDSS allows users to actively engage with
spatial data, conduct analyses, create visualizations, and explore diverse
decision scenarios (Fotheringham & Rogerson, 2009). Hence, conducting
a thorough analysis of the current spatial distribution and the implemen-
tation of a SDSS are crucial steps in promoting equal opportunities for
accessing high quality higher education across India.
In this paper, spatial analysis has been used to identify the clusters of dis-
tricts having a higher number of universities (hot spot) and regions with
no or lesser number of universities (cold spot). JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 4
4. DATA
The data used in the study are as follows:
4.1. All India Survey on Higher Education (AISHE)
The AISHE is an annual report, published by the Department of Higher Education,
Ministry of Education, Government of India. It is prepared based on information
on Higher Learning viz. Universities, Colleges, and Stand-Alone Institutions from
all over the country. The AISHE Report 2021-22 was released in 2024. For this
paper, the data available on the AISHE Dashboard as of August 2022 has been
used where the total numbers of Universities and Colleges were 1,160 and 47,497,
respectively.
4.2. Census of India
The 2011 Census of India (the 15th Indian Census) is used as the main secondary
source for all demographic data. It covers 28 states and 8 union territories, spread
across 640 districts.
4.3. Data Limitations
i. Number of Districts: It is important to mention that the geographical boundaries
of the districts in India are highly dynamic in nature. Also, the number of districts
keeps on changing over time due to administrative reasons. For the current paper,
733 districts in India have been considered (as of 2020). This has resulted in some
data mismatches and data discrepancies while working in connection with other
secondary data sources. It is important to keep this in mind while considering the
findings of the paper and in further policy making and implementation.
ii. Number of HEIs: The number of universities and colleges is constantly
evolving. According to the latest AISHE report (2021-22), there were 1,168
universities and 45,473 colleges. Given the dynamic nature of HEIs, the analysis
and recommendations provided should be considered indicative.
iii. Demographic Data: The demographic data used in this analysis is based on
the 2011 Census, which is out-of-date and creates a temporal mismatch when
compared to the 2022 data on HEIs. Nonetheless, it is worth noting that the
Ministry of Education also relied on 2011 Census data in its most recent AISHE
report (2021-22). To maintain consistency with government reporting, this paper
uses the same demographic data source. Establishing New Universities in India: An Evidence-based Suitability Analysis 5
5. KEY FINDINGS
The key findings of the availability and locations of universities in India are presented
below:
5.1. Higher Education Institutions in India
As per the AISHE data as of August 2022, there are 1,160 Universities in India. Out
of them, 52 are Central universities and 808 are State Public and Private Univer-
sities. There are a total of 152 Institutes of National Importance and 6 Institutes
under the State Legislature Act.
Establishing New Universities in India - An Evidence-based Suitability Analysis
6
As per the AISHE data as of August 2022, there are 1,160 Universities in India. Out of
them, 52 are Central universities and 808 are State Public and Private Universities.
There are a total of 152 Institutes of National Importance and 6 Institutes under the
State Legislature Act.
Figure 1: Types of Universities
Out of the 1,160 universities in India, 680 universities are located in urban areas, while
480 universities are in rural areas. This indicates that though 66% of the total
population lives in rural areas, 41% of universities are located in their geographic
proximity. Conversely, 59% of universities serve 34% of the population in urban areas.
The situation is different for colleges. Out of the 47,947 colleges in India, about 60% are
located in rural areas, while 40% are in urban areas. This distribution is due to historical
reasons and the concentration of economic and social resources in urban areas. It is
imperative to address this issue to ensure equitable access to higher education for
students living in both rural and urban areas.

152
6
52
418
390
34
10
80
1
1
16
0 50 100 150 200 250 300 350 400 450
Institute of National Importance
Institute under State Legislature Act
Central University
State Public University
State Private University
Deemed University-Government
Deemed University-Government Aided
Deemed University-Private
State Private Open University
Central Open University
State Open University
Figure 1: Types of Universities
Out of the 1,160 universities in India, 680 universities are located in urban areas,
while 480 universities are in rural areas. This indicates that though 66% of the total
population lives in rural areas, 41% of universities are located in their geographic
proximity. Conversely, 59% of universities serve 34% of the population in urban ar-
eas. The situation is different for colleges. Out of the 47,947 colleges in India, about
60% are located in rural areas, while 40% are in urban areas. This distribution is
due to historical reasons and the concentration of economic and social resources
in urban areas. It is imperative to address this issue to ensure equitable access to
higher education for students living in both rural and urban areas. -.&#-"#(! 1 (#0,-#.#- #( (# ?( 0#(??- /#.#&#.3 ?(&3-#- 6
Establishing New Universities in India - An Evidence-based Suitability Analysis
7

Figure 2: Location of Universities

5.1.1. State-level distribution
There is also a significant disparity in the distribution of universities among states
in India. Some states have a higher number of universities compared to others. For
instance, Rajasthan has 93 universities, Gujarat has 91, and Uttar Pradesh has 87.
However, in the Union Territories (UTs) of Andaman & Nicobar Islands,
Lakshadweep, Dadra and Nagar Haveli and Daman and Diu, there are no
universities. Other UTs and North-eastern states also have a relatively lower
number of universities, except for the National Capital Region of Delhi. This
disparity in the distribution of universities among states has resulted in uneven
access to higher education in different regions of the country.
41%
59%
66%
34%
RuralUrban
Percentage
of University
Percentage
of Population
Figure 2: Location of Universities
5.1.1. State-level Distribution
There is also a significant disparity in the distribution of universities among
states in India. Some states have a higher number of universities compared to
others. For instance, Rajasthan has 93 universities, Gujarat has 91, and Uttar
Pradesh has 87. However, in the Union Territories (UTs) of Andaman & Nico-
bar Islands, Lakshadweep, Dadra and Nagar Haveli and Daman and Diu, there
are no universities. Other UTs and North Eastern States also have a relatively
lower number of universities, except for the National Capital Region of Delhi.
This disparity in the distribution of universities among states has resulted in
uneven access to higher education in different regions of the country. Establishing New Universities in India: An Evidence-based Suitability Analysis 7
Figure 3: Number of Universities (State) -.&#-"#(!1(#0,-#.#-#( (#?(0#(??-/#.#&#.3?(&3-#- 8
Figure 4: Number of Colleges (State) Establishing New Universities in India: An Evidence-based Suitability Analysis 9
There is a significant disparity in the ratio of universities and colleges per
lakh population across different states in the country. Smaller and less
densely populated North Eastern States tend to have a higher ratio of
universities and colleges per lakh population. In contrast, larger and more
populous states like Uttar Pradesh and Bihar have fewer universities per
lakh population. States such as Andhra Pradesh and Chhattisgarh have
the lowest ratio of colleges per lakh population, highlighting regional vari-
ations in higher education accessibility.
5.1.2. District-level Distribution
The inequality in access to higher education in India is further highlight-
ed at the district level. While some districts have a high concentration of
universities, others have very limited access to higher education. Jaipur in
Rajasthan has the highest number of universities (35), followed by Ben-
galuru Urban in Karnataka (25), and Ahmedabad in Gujarat (21). There are
160 districts with only 1 university and 102 districts with less than 3 univer-
sities. As many as 380 districts mostly in Uttar Pradesh, Madhya Pradesh,
and the North Eastern States do not have any university.
Establishing New Universities in India - An Evidence-based Suitability Analysis
9
There is a significant disparity in the ratio of universities and colleges per lakh
population across different states in the country. Smaller and less densely
populated northeastern states tend to have a higher ratio of universities and
colleges per lakh population. In contrast, larger and more populous states like Uttar
Pradesh, and Bihar have fewer universities per lakh population. States such as
Andhra Pradesh and Chhattisgarh have the lowest ratio of colleges per lakh
population, highlighting regional variations in higher education accessibility.
5.1.2. District-level distribution
The inequality in access to higher education in India is further highlighted at the
district level. While some districts have a high concentration of universities, others
have very limited access to higher education. Jaipur in Rajasthan has the highest
number of universities (35), followed by Bengaluru Urban in Karnataka (25), and
Ahmedabad in Gujarat (21). There are 160 districts with only 1 university and 102
districts with less than 3 universities. As many as 380 districts mostly in Uttar
Pradesh, Madhya Pradesh, and the Northeastern states do not have any university.

Figure 5: Distribution of Universities by Districts

Figure 6: Distribution of Colleges by Districts
160
102
34 35
18
4
0
40
80
120
160
200
123456
NUMBER OF DISTRICTS
NUMBER OF UNIVERSITIES
Figure 5: Distribution of Universities by Districts
Establishing New Universities in India - An Evidence-based Suitability Analysis
9
There is a significant disparity in the ratio of universities and colleges per lakh
population across different states in the country. Smaller and less densely
populated northeastern states tend to have a higher ratio of universities and
colleges per lakh population. In contrast, larger and more populous states like Uttar
Pradesh, and Bihar have fewer universities per lakh population. States such as
Andhra Pradesh and Chhattisgarh have the lowest ratio of colleges per lakh
population, highlighting regional variations in higher education accessibility.
5.1.2. District-level distribution
The inequality in access to higher education in India is further highlighted at the
district level. While some districts have a high concentration of universities, others
have very limited access to higher education. Jaipur in Rajasthan has the highest
number of universities (35), followed by Bengaluru Urban in Karnataka (25), and
Ahmedabad in Gujarat (21). There are 160 districts with only 1 university and 102
districts with less than 3 universities. As many as 380 districts mostly in Uttar
Pradesh, Madhya Pradesh, and the Northeastern states do not have any university.

Figure 5: Distribution of Universities by Districts

Figure 6: Distribution of Colleges by Districts
160
102
34 35
18
4
0
40
80
120
160
200
123456
NUMBER OF DISTRICTS
NUMBER OF UNIVERSITIES
Figure 6: Distribution of Colleges by Districts
Bengaluru Urban in Karnataka has 1,118 colleges, followed by Jaipur in Ra-
jasthan (740) and Pune in Maharashtra (628). 153 districts in India have
100 or more colleges. On the other hand, 29 districts do not have a single
college and 85 districts have less than 5 colleges. Establishing New Universities in India: An Evidence-Based Suitability Analysis 10
Establishing New Universities in India - An Evidence-based Suitability Analysis
10

Figure 6: Distribution of Colleges by Districts
Bengaluru Urban in Karnataka has 1,118 colleges, followed by Jaipur in Rajasthan
(740) and Pune in Maharashtra (628). 153 districts in India have 100 or more colleges.
There are 85 districts with less than 5 colleges. 29 districts do not have a college in
India, while 148 districts have 10 or fewer colleges.

Figure 7: Number of Universities (Districts)
*As of August 2022

Figure 7: Number of Universities (Districts) Establishing New Universities in India: An Evidence-based Suitability Analysis 11
Establishing New Universities in India - An Evidence-based Suitability Analysis
11

Figure 8: Number of Colleges (District)

5.1.3. Remote regions
There are 169 districts in the remote regions of India that deserve special attention,
comprising the Himalayan states, the northeast region, and the islands spread
across 14 states & UTs. There are a total of 167 universities and 2,293 colleges in this
region. Dehradun in Uttarakhand has the highest 19 universities and 132 colleges.
On the contrary, 116 districts do not have a university, while 23 districts do not have
any college. The list of districts in the remote regions with the number of
universities and colleges is given in Annexure– I.

5.1.4. Aspirational Districts
Out of the 112 Aspirational Districts in India, 43 districts have at least one university,
with Ranchi in Jharkhand having the highest, 18 universities. The remaining 74
districts do not have any universities. The list of 112 Aspirational districts with the
number of universities and colleges is given in Annexure– II.

Figure 8: Number of Colleges (Districts) -.8C#-"#E! N 2E#0I-#.#- #E E#8 ?E 0#E:??8- 0L#.8#C#.P ?E8CP-#- 12
5.1.3. Remote Regions
There are 169 districts in the remote regions of India comprising the Hi-
malayan states, the North East Region, and the islands spread across 14
states & UTs that deserve special attention. There are a total of 167 uni-
versities and 2,293 colleges in this region. Dehradun in Uttarakhand has 19
universities and 132 colleges, the highest in this region. 116 districts do not
have a university, while 23 districts do not have any college. The list of dis-
tricts in the remote regions with the number of universities and colleges in
each is given in Annexure– I. Establishing New Universities in India - An Evidence-based Suitability Analysis
??

Figure 9: Number of Universities in Remote Regions

Figure 10: Number of Universities in Aspirational Districts

Figure 9: Number of Universities in Remote Regions Establishing New Universities in India: An Evidence-based Suitability Analysis 13
5.1.4. Aspirational Districts
Out of the 112 Aspirational Districts in India, 43 districts have at least one
university, with Ranchi in Jharkhand having 18 universities, the highest
among the Aspirational Districts. The remaining 74 districts do not have
any university. The list of 112 Aspirational Districts with the number of uni-
versities and colleges in each is given in Annexure– II.Establishing New Universities in India - An Evidence-based Suitability Analysis
??

Figure 9: Number of Universities in Remote Regions

Figure 10: Number of Universities in Aspirational Districts

Figure 10: Number of Universities in Aspirational Districts -.8C#-"#E! N 2E#0I-#.#- #E E#8 ?E 0#E:??8- 0L#.8#C#.P ?E8CP-#- 14
5.1.5. Temporal Changes
In recent years, the number of universities has been rapidly growing in In-
dia, from a total of 755 in 2015-16 to 1,160 in 2022. The number of Institutes
of National Importance has increased by more than double since 2015-16,
followed by a substantial growth in the number of State Private Univer-
sities. However, a more systematic approach is required to make higher
education accessible to all in terms of spatial locations.
Figure 11: Number of Major Universities by Type (2015-22) Establishing New Universities in India: An Evidence-based Suitability Analysis 15 Establishing New Universities in India - An Evidence-based Suitability Analysis
??

Figure 12: Location of Universities by Type

Figure 12: Location of Universities by Type -.&#-"#(! 1 (#0,-#.#- #( (# ?( 0#(??- /#.#&#.3 ?(&3-#- 16
Establishing New Universities in India - An Evidence-based Suitability Analysis
15

Figure 13: Districts without any University

380
Number of Districts
353
Without any university 1 or more university
Figure 13: Districts without any University Establishing New Universities in India: An Evidence-based Suitability Analysis 17
5.2. Socio-demographic Characteristics
5.2.1. Number of Universities and Colleges per lakh population (18-23 Years)
The number of universities and colleges per lakh population in the age group
of 18-23 years in India is 0.8 and 33, respectively. However, the ratio varies
greatly among the states. The number of universities per lakh population in
Chandigarh is the highest (17), whereas this ratio is less than 1 in 3 states
namely Bihar, Uttar Pradesh, Chhattisgarh. The number of colleges per lakh
population was 1 in Chhattisgarh and Andhra Pradesh, and as high as 1,692 in
Arunachal Pradesh.
Figure 14: Number of Universities per lakh population (18-23 years) -.8C#-"#E! N 2E#0I-#.#- #E E#8 ?E 0#E:??8- 0L#.8#C#.P ?E8CP-#- 18
Figure 15: Number of Colleges per lakh population (18-23 years)
5.2.2. Gross Enrolment Ratio (GER) and Gender Parity Index (GPI)
As stated earlier, in 2021-22, GER in India was 28.4% in higher education
(age group of 18 to 23 years). Female GER (28.5%) was marginally higher
than that of males (28.3%). However, 17 states and UTs had less GER than
the national average. Among the larger states, Tamil Nadu had recorded Establishing New Universities in India: An Evidence-based Suitability Analysis 19
a GER of 47%. The national average GPI was 1.01 in 2021-22. A total of 10
states had recorded a GPI of less than 1. Tripura, Sikkim, and Bihar record-
ed the lowest GPI in higher education. On the contrary, the smaller states
and UTs had higher GPI, with Lakshadweep at the top, with a GPI of 6.33.
Figure 16: State-wise Gross Enrolment Ratio Establishing New Universities in India: An Evidence-Based Suitability Analysis 20
Figure 17: State-wise Gender Parity Index Establishing New Universities in India: An Evidence-based Suitability Analysis 21
6. RECOMMENDATIONS USING SPATIAL
ANALYSIS
This paper aims to provide evidence-based recommendations for analysing the current
spatial distribution of HEIs throughout the country and identifying optimal locations for
establishing new universities at a macro scale. The study utilizes spatial analysis tech-
niques in conjunction with relevant secondary datasets to gain insights into the spatial
patterns and make recommendations regarding the location of new universities. These
are made at 3 spatial scales, as elaborated below.
6.1. District-level Recommendations
6.1.1. Capital Cities of States/ UTs without a University
Overall, 3 UTs in India do not have any universities, namely the Andaman
and Nicobar Islands, Dadra and Nagar Haveli and Daman and Diu, and
Lakshadweep. It is, therefore recommended to set up a new university(s)
or convert an affiliating college into an autonomous degree granting HEI
at the capital city of the respective UT. The details of these UTs are given
in Annexure - III.
6.1.2. Districts without universities but having more than 50 colleges
Out of the total 380 districts that do not have any university, 81 districts
have 50 or more colleges. Most of these districts are neither part of the remote
regions nor are they very sparsely populated. The considerably higher number
of colleges establishes the fact that there is demand for higher education
facilities there. Therefore, these districts (Figure 18) are recommended
for setting up new cluster universities. The list of these districts is in
Annexure- IV.
6.2. Region-level Recommendations
From the map (Figure 19) showing the current location of universities, it is quite
evident that they are neither randomly nor evenly located over space, rather they
are clustered in and around a few urban centres. In other words, districts with a
higher number of universities are grouped together while there are multiple pock-
ets of districts with no or lesser number of universities. -.&#-"#(!1(#0,-#.#-#( (#?(0#(??-/#.#&#.3?(&3-#- 22
Figure 18: Districts without any Universities, having more than 50 Colleges
(* Not Considered refers to districts that either already have at least one university,
or have no universities but fewer than 50 colleges.) Establishing New Universities in India: An Evidence-based Suitability Analysis 23
To analyse the spatial distribution of universities across the country and quantify
the degree of concentration, a spatial statistical tool, namely spatial autocorrela-
tion has been used. The term spatial autocorrelation refers to the presence of sys-
tematic spatial variation in a mapped variable. It is the correlation among values of
a single variable strictly attributable to their relatively close locational positions on
a two-dimensional surface. Positive spatial autocorrelation means that geograph-
ically nearby values of a variable tend to be similar on a map: high values tend to
be located near high values, and low values near low values.
Figure 19: Location of current Universities JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 24
The most common way for testing spatial autocorrelation is the Moran’s I statistic.
It is designed to reject the null hypothesis of spatial randomness in favour of an al-
ternative of clustering. However, clustering is a characteristic of the complete spa-
tial pattern and does not indicate the location of the clusters. In order to identify
the actual cluster locations in the dataset, Local Indicators of Spatial Association
(LISA) were proposed by Luc Anselin in 1995. A LISA is seen as having two import-
ant characteristics. First, it provides a statistic for each location with an assess-
ment of significance. Second, it establishes a proportional relationship between
the sum of the local statistics and a corresponding global statistic. Local Moran’s I
statistic is the most widely used LISA statistic that describes spatial clustering of
observations in high or low values. The formula for Moran’s I statistic for an obser-
vation at location i is as follows:
Where:
z
i
is x
i
– x
-
, the mean of variable x
wij as the elements of the spatial weights matrix,
S
0
=∑
i
∑
j
wij is the sum of all the weights
n is the number of observations
With the row-standardized weights, the sum of all weights, S
0
equals the number of
observations, n. As a result, a corresponding Local Moran’s I statistic would consist
of the component in the double sum that corresponds to each observation i, or:
i.e., the product of the value at location i with its spatial lag, the weighted sum of
the values at neighbouring locations.
The Univariate Local Moran’s I was calculated for the district map with the num-
ber of universities in each district as the variable with ‘queen contiguity’ for the
spatial weight matrix. The LISA cluster map along with the Moran Scatter Plot was
generated using Geoda, a free and open-source software tool for spatial data sci-
ence. The Moran Scatter Plot displays the relationship between the variable value
at observation i and the average variable value in the neighbourhood, organized
into four quadrants: High-High, High-Low, Low-Low, and Low-High. Local Moran’s
I outcome is then colour-coded, based on scatterplot quadrant, onto a choropleth
map illustrating spatial clusters. Establishing New Universities in India: An Evidence-based Suitability Analysis 25
Establishing New Universities in India - An Evidence-based Suitability Analysis
23

Figure 20: District Connectivity Histogram
The connectivity histogram is generated using the queen criterion that determines
neighbouring units as those that have any point in common, including both common
boundaries and common corners.
The Moran Scatter Plot shows the classification of spatial association into four categories,
corresponding to the location of the points in the four quadrants of the plot.

Figure 21: Moran Scatter Plot
This cluster map augments the significant locations with an indication of the type of
spatial association, based on the location of the value and its spatial lag in the Moran
Scatter Plot. Here, all four categories are represented, with the high-high clusters (52), the
Number of Neighbours
Frequency

Spatial Cluster
(High-High)
Spatial Cluster
(Low-Low)
Spatial Outlier
(Low-High)
Spatial Outlier
(High-Low)
Figure 20: District Connectivity Histogram
The connectivity histogram is generated using the queen criterion that determines
neighbouring units as those that have any point in common, including both common
boundaries and common corners.
The Moran Scatter Plot shows the classification of spatial association into four categories,
corresponding to the location of the points in the four quadrants of the plot
.

Figure 21: Moran Scatter Plot
This cluster map augments the significant locations with an indication of the type
of spatial association, based on the location of the value and its spatial lag in the
Moran Scatter Plot. Here, all four categories are represented, with the high-high
clusters (52), the low-low clusters (76), the low-high spatial outliers (68), and the
high-low spatial outliers (29). A total of 503 districts are found not significant here. JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 26
At the regional level, the analysis is used to identify the group of districts with a
low number of universities surrounded by similar districts, where the setting up
of new universities is recommended. These regions include the North East States
particularly Arunachal Pradesh, Assam, and Manipur, the eastern part of Maha-
rashtra, and southwestern part of Odisha, the eastern part of Madhya Pradesh
with neighbouring southeastern part of Uttar Pradesh and Bihar, and the eastern
districts of Gujarat along with neighbouring districts of western Madhya Pradesh.
The state-wise details of the districts in these regions are given in Annexure – V.
Establishing New Universities in India - An Evidence-based Suitability Analysis
??
low-low clusters (76), the low-high spatial outliers (68), and the high-low spatial outliers
(29). A total of 503 districts are found not significant here.
At the regional level, the analysis is used to identify the group of districts with a low
number of universities surrounded by similar districts, where the setting up of new
universities is recommended. These regions include the Northeast states particularly
Arunachal Pradesh, Assam, and Manipur, the eastern part of Maharashtra, and
southwestern part of Odisha, the eastern part of Madhya Pradesh with neighbouring
southeastern part of Uttar Pradesh and Bihar, and the eastern districts of Gujarat along
with neighbouring districts of western Madhya Pradesh. The state-wise details of the
districts in these regions are given in Annexure ? V.

Figure 22: Local Spatial Autocorrelation (Moran's I)

Figure 22: Local Spatial Autocorrelation (Moran’s I) Establishing New Universities in India: An Evidence-based Suitability Analysis 27
6.3. State-level Recommendations
The GER and the number of Higher Education facilities per lakh population in the
18-23 years age group are considered to identify the states that require more uni-
versities on a priority basis.
According to United Nations Educational, Scientific and Cultural Organization
(UNESCO), GER is defined as the total enrolment in a specific level of education,
regardless of age, expressed as a percentage of the eligible age-group population
corresponding to the same level of education in a given school year. The formula
for GER is as follows:
Establishing New Universities in India - An Evidence-based Suitability Analysis
25
6.3. State-level recommendations
The GER and the number of Higher Education facilities per lakh population in the 18-
23 years age group are considered to identify the states that require more universities
on a priority basis.
As per the United Nations Educational, Scientific and Cultural Organization (UNESCO),
GER is defined as the total enrolment in a specific level of education, regardless of age,
expressed as a percentage of the eligible age-group population corresponding to the
same level of education in a given school year. The formula for GER is as follows:

The University Ratio (UR) and College Ratio (CR) per lakh population (18-23 years age
group) are derived by dividing the number of universities and colleges respectively by
the total population of the corresponding state. In the beginning, all 3 factors, viz. GER,
UR and CR are normalized using the following formulae:
z
i = (xi – min(x)) / (max(x) – min(x))
where:
z
i: The i
th
normalized value in the dataset
x
i: The i
th
value in the dataset
min(x): The minimum value in the dataset
max(x): The maximum value in the dataset

The percentage of the respective state population aged between 18 – 23 years to the
total national population (TNP) in the same age group and to the state total
population (TSP) was then calculated.
Finally, the composite score of states is calculated using the following formulae:
S
i = (1 - (GERn * 0.4) + (URn * 0.15) + (CRn * 0.15)) + (TNPp * 0.15) + (TSPp * 0.15))
where:
Si: The ith composite score in the dataset
Enrolment in a specific level of education
Population of official age-group for the specific level of education
GER = X 100
The University Ratio (UR) and College Ratio (CR) per lakh population (18-23 years
age group) are derived by dividing the number of universities and colleges respec-
tively by the total population of the corresponding state. In the beginning, all 3
factors, viz. GER, UR and CR are normalized using the following formulae:
z
i
= (x
i
– min(x)) / (max(x) – min(x))
where:
z
i
: The ith normalized value in the dataset
x
i
: The ith value in the dataset
min(x): The minimum value in the dataset
max(x): The maximum value in the dataset
The percentage of the respective state population aged between 18 – 23 years to the
total national population (TNP) in the same age group and to the total state population
(TSP) was then calculated.
Finally, the composite score of states is calculated using the following formulae:
S
i
= (1 - (GERn * 0.4) + (URn * 0.15) + (CRn * 0.15)) + (TNPp * 0.15) + (TSPp * 0.15))
where:
S
i
: The ith composite score in the dataset
GERn: Normalised GER of the ith item in the dataset
URn: Normalised university count per lakh population of the ith item in the dataset
CRn: Normalised college count per lakh population of the ith item in the dataset
TNPp: Percentage of the population to the national population of the ith item in the
dataset
TSPp: Percentage of the population to the state population of the ith item in the dataset
TSPp: Percentage of the population to the state population of the ith item in the dataset JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 28
By sorting, the states/ UTs in ascending order of the composite score produce
the list where the GER and the ratio of Colleges and Universities are low, but the
population is high in the age group of 18-23 years. Therefore, states with higher
composite scores will require more new universities to meet the demand.
This analysis shows that Uttar Pradesh, the most populous state in India, needs
the highest number of new universities to meet its demand. Next to it, other larg-
er states with higher populations like Maharashtra, West Bengal, Bihar, Rajasthan,
and Madhya Pradesh are at a higher priority.
The complete calculation is given in Annexure – VI.
Figure 23: States requiring more Universities (as per the Composite Score) Establishing New Universities in India: An Evidence-based Suitability Analysis 29
7. WAY FORWARD
This paper provides a macro-scale analysis of the current distribution of universities
across India and offers strategic recommendations for establishing new universities at
district, regional, and state levels. The underlying data has been integrated into a dy-
namic spatial database, designed to be updated as more recent data becomes available.
This database can be leveraged to develop a rule-based Spatial Decision Support Sys-
tem (SDSS) to enable policymakers and administrators in making informed, data-driven
decisions. Additionally, if needed, an interactive online dashboard could be developed to
enable real-time data and spatial analysis, enhancing the accessibility and applicability
of these insights for ongoing planning and development.
The recommendations in this paper primarily focus on a macro scale, prioritizing states
and districts across the country that require new universities. However, when planning
for the development or expansion of universities, both greenfield and brownfield proj-
ects, it is crucial to consider several associated factors for their successful establishment.
In addition to the macro-level analysis, further in-depth micro-scale studies are neces-
sary while considering the following factors:
i. Location: Consider factors such as accessibility, transportation connectivity, and
proximity to student populations.
ii. Demographics and Regional Demand: Analyse the demographics of the region
and assess the demand for higher education.
iii. Infrastructure and Facilities: Evaluate the availability of infrastructure and facili-
ties necessary for a university, including classrooms, laboratories, libraries, admin-
istrative buildings, student accommodations, and recreational spaces.
iv. Environmental Considerations: Prepare development plans by taking into account
the environmental characteristics of the site, including topography, climate, and
ecological factors.
v. Social and Cultural Context: Ensure that the university’s mission and programmes
reflect and respect the local context, promoting inclusivity and diversity within
the campus community.
vi. Future Growth Potential: Evaluate the potential for future growth and expansion
of the university.
Further, some specific pilot studies can be taken up for the following:
7.1 Special attention for improving Gender Parity Index
Gender Parity Index (GPI) in education is the ratio of the number of female stu-
dents enrolled at a specific level of education to the number of male students at
the same level. The formula for GPI is as follows: Establishing New Universities in India: An Evidence-Based Suitability Analysis 30

While comparing the GPI for the age group 18 to 23 years with the sex ratio (per male)
in states, it was found that many large and populous states have lower GPI in higher
education than their respective sex ratio. Such states include Maharashtra, Bihar, West
Bengal, Tamil Nadu, among others. Therefore, it is recommended to take up appropriate
actions to encourage the equitable participation of girls in the Higher Education system
in such states/UTs. The Government of India and the respective state governments may
consider introducing targeted schemes to improve the GPI in higher education bridging
the social and gender gaps in line with Samagra Shiksha - an Integrated Scheme for
School Education (ISSE).
.
Figure 24: Comparison of GPI in Higher Education and Sex Ratio
7.2. Tribal/ Forest/ Hilly Districts for Specialized Universities
In most districts in the hilly regions and the tribal areas, the number of universities
is relatively low. In order to encourage the local people for Higher Education, it is Establishing New Universities in India: An Evidence-based Suitability Analysis 31
recommended to set up specialized universities and departments in such areas,
considering the local environment and demography. These may include Himalayan
studies, Tribal studies, Culture, Handicrafts, Tourism, Local Language and Religion,
etc. However, further research is recommended to identify and correlate the loca-
tions, specializations, and demand.
7.3. Technical University/ Vocational Training Institutes in Industrial Regions
A total of 34 universities and 113 Institutes of National Importance exists in India
with specialization in technical studies. It is further recommended to take up a
more detailed study to map the major industrial regions in India with their special-
izations and identify the gap in the supply of local skilled human resources. Based
on such studies, new universities with selective specialization may be set up to
fulfil the local talent demand.
Figure 25: Location of Technical Universities and Institutes of National Importance JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 32
8. CONCLUSION
NEP 2020 envisages an increase in the GER in Indian Higher Education for the age group
of 18-23 years to 50% by 2035. To take on board this quantum of students (about 4.5
crore additional students), many HEIs will have to plan both greenfield and brownfield
campuses.
Several new universities including cluster universities will also need to be established.
Identifying the optimal location for establishing new universities would entail careful
consideration of a range of factors like regional demand, accessibility and proximity
of nearby universities, collaboration opportunities with existing academic institutions,
research organizations, and industries in the region, and the socio-economic
characteristics of the locality.
The analysis of the spatial distribution of existing universities and colleges must play a
crucial role in planning for the future growth plan. To ensure an effective and inclusive
expansion of infrastructure, it is essential to adopt a data-driven approach.
This paper proposes the use of spatial and statistical analysis as a lens for infrastructure
planning in higher education in India. By utilizing scientific and evidence-based methods,
this approach will facilitate informed decision-making and promote equitable access
to higher education across the country. As new institutions emerge, both greenfield
and brownfield, this methodology will support the efficient allocation of resources and
promote balanced educational opportunities for all, across regions. Establishing New Universities in India: An Evidence-based Suitability Analysis 33
9. REFERENCES AND BIBLIOGRAPHY
1. Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geographical Anal-
ysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
2. Chang, Kang-Tsung (2019). Geographic Information System. In International Ency-
clopedia of Geography, 1–10. Wiley https://doi.org/10.1002/9781118786352.wbieg0152.
pub2
3. De Smith, M. J., Goodchild, M. F., & Longley, P. A. (2007). Geospatial Analysis: a com-
prehensive guide to principles, techniques and software tools, Troubador Publishing.
http://ci.nii.ac.jp/ncid/BA82011294
4. Fotheringham, A. S., & Rogerson, P. A. (2009). The SAGE Handbook of Spatial Anal-
ysis. In SAGE Publications Ltd eBooks. https://doi.org/10.4135/9780857020130
5. Paramasivam, C., & Venkatramanan, S. (2019). An Introduction to Various Spatial
Analysis Techniques. GIS And Geostatistical Techniques for Groundwater Science,
23–30. Elsevier. https://doi.org/10.1016/b978-0-12-815413-7.00003-1
6. The Multiple Facets of Correlation Functions. Data Analysis Techniques
for Physical Scientists, 526–576. Cambridge University Press. https://doi.
org/10.1017/9781108241922.013
7. Transforming Unstructured Data into Useful Information. (2014, March 12). Big Data,
Mining, and Analytics, 227–246. Auerbach Publications. https://doi.org/10.1201/
b16666-14 JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 34
10. REPORTS AND PORTALS
1. All India Survey on Higher Education (AISHE). Retrieved August 25, 2022, from
https://aishe.gov.in/aishe/home
2. Dempsey, C. (2021, September 29). What is GIS? GIS Lounge. Retrieved September
15, 2023, from https://www.gislounge.com/what-is-gis/
3. Districts - Know India: National Portal of India. Retrieved May 20, 2023, from https://
knowindia.india.gov.in/districts/
4. Documentation | GeoDa on GitHub. Retrieved September 9, 2023, from https://ge-
odacenter.github.io/documentation.html
5. Gross enrolment ratio | UNESCO UIS. Retrieved August 19, 2023, from https://uis.
unesco.org/en/glossary-term/gross-enrolment-ratio
6. National Education Policy. (2020). In Ministry of Human Resource Development.
MHRD, GoI. Retrieved May 18, 2023, from https://www.education.gov.in/sites/up-
load_files/mhrd/files/NEP_Final_English_0.pdf
7. NIC, L. P. Statistics New | Government of India, Ministry of Education. Ministry of
Education. Retrieved September 1, 2023, from https://www.education.gov.in/statis-
tics-new?shs_term_node_tid_depth=387
8. Panjwani, T. (2018, March 26). Higher Education in India. UK India Business Council.
Retrieved May 12, 2023, from https://www.ukibc.com/higher-education-in-india/
9. PCMag. Definition of spatial analysis. PCMAG. Retrieved September 9, 2023, from
https://www.pcmag.com/encyclopedia/term/spatial-analysis Establishing New Universities in India: An Evidence-based Suitability Analysis 35
Annexure I: List of Districts in remote regions
#
with the
number of universities and colleges
S. No. DistrictState
Number of
College University
1Nicobars
ANDAMAN &
NICOBAR
4 0
2 North and Middle Andaman1 0
3 South Andamans4 0
4 Anjaw
ARUNACHAL
PRADESH
0 0
5 Changlang2 0
6 Dibang Valley1 0
7 East Kameng1 0
8 East Siang4 1
9 Kamle0 0
10Kra Daadi0 0
11Kurung Kumey1 0
12Leparada0 0
13Lohit4 1
14Longding0 0
15Lower Dibang Valley2 0
16Lower Siang0 0
17Lower Subansiri3 1
18Namsai0 0
19Pakke Kessang0 0
20Papum Pare14 6
21Shi Yomi0 0
22Siang1 0
23Tawang1 0
24Tirap1 0
25Upper Siang1 0
26Upper Subansiri1 0
27West Kameng1 0
28West Siang4 1
29Baksa
ASSAM
13 0
30Barpeta32 2
31Biswanath0 0
32Bongaigaon12 0
33Cachar31 2
34Charaideo7 0
35Chirang8 0 JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 36
S. No. DistrictState
Number of
College University
36Darrang
ASSAM
12 0
37Dhemaji25 0
38Dhubri19 0
39Dibrugarh22 1
40Dima Hasao1 0
41Goalpara16 0
42Golaghat19 0
43Hailakandi9 0
44Hojai3 1
45Jorhat27 4
46Kamrup101 6
47Kamrup Metro35 6
48Karbi Anglong15 0
49Karimganj17 0
50Kokrajhar23 2
51Lakhimpur27 1
52Majuli6 1
53Morigaon11 0
54Nagaon39 1
55Nalbari13 1
56Sivasagar14 0
57Sonitpur22 1
58South Salmara Mancachar0 0
59Tinsukia17 0
60Udalguri11 0
61West Karbi Anglong1 0
62Bilaspur
HIMACHAL
PRADESH
0 0
63Chamba16 0
64Hamirpur69 3
65Kangra83 4
66Kinnaur2 0
67Kullu13 0
68Lahaul And Spiti1 0
69Mandi54 4
70Shimla51 3
71Sirmaur30 2
72Solan43 11
73Una22 2 Establishing New Universities in India: An Evidence-based Suitability Analysis 37
S. No. DistrictState
Number of
College University
74Anantnag
JAMMU AND
KASHMIR (UT)
17 0
75Budgam20 0
76Bandipora13 0
77Baramulla42 0
78Doda8 0
79Ganderbal9 0
80Jammu69 5
81Kathua29 0
82Kishtwar6 0
83Kulgam7 0
84Kupwara10 0
85Mirpur0 0
86Muzaffarabad0 0
87Pulwama20 1
88Poonch7 0
89Rajouri16 1
90Ramban5 0
91Reasi8 1
92Samba10 2
93Shopian4 0
94Srinagar46 6
95Udhampur15 0
96Lakshadweep District LAKSHADWEEP 3 0
97Kargil
LADAKH
3 0
98Leh ladakh2 2
99East Garo Hills
MEGHALAYA
3 0
100East Jaintia Hills3 0
101East Khasi Hills42 6
102North Garo Hills0 0
103Ri Bhoi8 3
104South Garo Hills1 0
105South West Garo Hills0 0
106South West Khasi Hills1 0
107West Garo Hills13 1
108West Jaintia Hills5 1
109West Khasi Hills6 0
110Bishnupur
MANIPUR
10 0
111Chandel3 0 JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 38
S. No. DistrictState
Number of
College University
112Churachandpur
MANIPUR
6 0
113Imphal East20 1
114Imphal West36 6
115Jiribam1 0
116Kakching0 0
117Kamjong0 0
118Kangpokpi2 0
119Noney0 0
120Pherzawl0 0
121Senapati13 1
122Tamenglong2 0
123Tengnoupal1 0
124Thoubal10 1
125Ukhrul3 0
126Aizawl
MIZORAM
25 3
127Champhai2 0
128Kolasib2 0
129Lawngtlai2 0
130Lunglei4 0
131Mamit2 0
132Siaha1 0
133Serchhip2 0
134Dimapur
NAGALAND
27 5
135Kiphire1 0
136Kohima23 0
137Longleng1 0
138Mokokchung6 0
139Mon2 0
140Peren3 0
141Phek2 0
142Tuensang3 0
143Wokha2 0
144Zunheboto1 1
145Gangtok
SIKKIM
22 6
146Mangan0 0
147Namchi4 2
148Gyalshing4 0 Establishing New Universities in India: An Evidence-based Suitability Analysis 39
S. No. DistrictState
Number of
College University
149Dhalai
TRIPURA
4 0
150Gomati1 0
151Khowai0 0
152North Tripura6 0
153Sepahijala0 0
154South Tripura5 0
155Unakoti1 0
156West Tripura38 5
157Almora
UTTARAKHAND
18 2
158Bageshwar6 0
159Chamoli14 0
160Champawat11 0
161Dehradun132 19
162Pauri Garhwal25 5
163Haridwar128 8
164Nainital37 2
165Pithoragarh12 0
166Rudra Prayag7 0
167Tehri Garhwal22 1
168Udam Singh Nagar74 2
169Uttar Kashi10 0
#
The remote regions of India that deserve special attention, comprising the Himalayan
states, the northeast region, and the islands spread across 14 states and UTs
* In alphabetical order of state/UT names; District names are as per Integrated Government
Online Directory Establishing New Universities in India: An Evidence-Based Suitability Analysis 40
Annexure II: List of Aspirational Districts with the number
of universities and colleges
S. No. DistrictState
Number of
College University
1Y.S.R.
ANDHRA
PRADESH
191 3
2 Visakhapatnam216 6
3 Vizianagaram142 0
4 Namsai
ARUNACHAL
PRADESH
0 0
5 Baksa
ASSAM
13 0
6 Barpeta32 2
7 Darrang12 0
8 Goalpara16 0
9 Hailakandi9 0
10Dhubri19 0
11Udalguri11 0
12Araria
BIHAR
11 0
13Aurangabad291 0
14Banka18 0
15Begusarai22 0
16Gaya60 3
17Jamui11 0
18Katihar19 1
19Khagaria12 0
20Muzaffarpur59 1
21Purnia24 1
22Sheikhpura8 0
23Sitamarhi18 0
24Nawada23 0
25Korba
CHHATTISGARH
28 0
26Mahasamund22 0
27Rajnandgaon51 1
28Balrampur29 0
29Bastar24 1
30Sukma5 0
31Narayanpur4 0
32Dakshin Bastar Dantewada11 0
33Kondagaon7 0
34Uttar Bastar Kanker8 0 Establishing New Universities in India: An Evidence-based Suitability Analysis 41
S. No. DistrictState
Number of
College University
35Dahod
GUJARAT
35 0
36Narmada17 1
37Chamba
HIMACHAL
PRADESH
16 0
38NuhHARYANA17 0
39Bokaro
JHARKHAND
26 0
40 Chatra5 0
41Dumka12 1
42Garhwa18 0
43Godda7 0
44Gumla8 0
45Hazaribagh26 2
46Latehar3 0
47Lohardaga3 0
48Pakur3 0
49Palamu23 2
50Pashchimi Singhbhum15 1
51Purbi Singhbhum33 1
52Ramgarh11 1
53Ranchi61 18
54Sahibganj5 0
55Simdega3 0
56Khunti2 0
57Giridih18 0
58BaramullaJAMMU AND
KASHMIR (UT)
42 0
59Kupwara10 0
60Raichur
KARNATAKA
162 3
61Yadgir101 0
62WayanadKERALA 36 1
63Gadchiroli
MAHARASHTRA
77 1
64Nandurbar46 0
65Washim40 0
66Osmanabad67 0
67Ri BhoiMEGHALAYA 8 3
68ChandelMANIPUR3 0 JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 42
S. No. DistrictState
Number of
College University
69 Barwani
MADHYA
PRADESH
37 0
70 Chhatarpur113 2
71Damoh43 1
72Guna34 1
73Rajgarh40 0
74Singrauli32 0
75Vidisha70 1
76Khandwa24 1
77MamitMIZORAM2 0
78KiphireNAGALAND1 0
79Dhenkanal
ODISHA
33 0
80 Balangir32 1
81Gajapati14 1
82 Kalahandi38 1
83 Kandhamal21 0
84 Koraput21 1
85 Malkangiri8 0
86 Nabarangpur11 0
87Nuapada11 0
88 Rayagada23 1
89 Moga
PUNJAB
49 0
90 Firozpur39 1
91Baran
RAJASTHAN
31 0
92 Dholpur57 0
93 Jaisalmer15 0
94 Karauli43 0
95 Sirohi29 1
96 GyalshingSIKKIM 4 0
97Khammam
TELANGANA
123 0
98 Kumuram Bheem Asifabad4 0
99 Jayashankar Bhupalapally3 0
100Virudhunagar
TAMIL NADU
63 1
101Ramanathapuram40 0
102DhalaiTRIPURA 4 0 Establishing New Universities in India: An Evidence-based Suitability Analysis 43
S. No. DistrictState
Number of
College University
103Haridwar
UTTARAKHAND
128 8
104Udam Singh Nagar74 2
105Bahraich
UTTAR PRADESH
45 0
106Balrampur10 0
107Chandauli57 0
108Chitrakoot27 1
109Fatehpur85 0
110Shrawasti11 0
111Siddharthnagar43 1
112Sonbhadra47 0
* In alphabetical order of state/UT names; District names are as per Integrated Government
Online Directory -.8C#-"#E! N 2E#0I-#.#- #E E#8 ?E 0#E:??8- 0L#.8#C#.P ?E8CP-#- 44
Annexure III: List of UTs without any university
S. No.Union TerritoryNumber of Colleges
1 Andaman and Nicobar Islands9
2 Lakshadweep3
3 Dadra and Nagar Haveli and Daman and Diu21
*In alphabetical order of UT names Establishing New Universities in India: An Evidence-based Suitability Analysis 45
Annexure IV: Districts without any universities but having
more than 50 colleges
S. No.DistrictState No of Colleges
1 PrakasamANDHRA
PRADESH
251
2 Vizianagaram142
3 AurangabadBIHAR291
4 Janjgir-ChampaCHHATTISGARH70
5 Arvalli
GUJARAT
69
6 Gir Somnath57
7 Amreli53
8 YamunanagarHARYANA56
9 Yadgir
KARNATAKA
101
10 Hassan88
11 Chitradurga84
12 Uttara Kannada75
13 Koppal67
14 Chikkaballapura62
15 Alappuzha
KERALA
80
16 Pathanamthitta79
17 Idukki61
18 Bhind
MADHYA
PRADESH
108
19 Morena105
20 Betul77
21 Hoshangabad62
22 Panna53
23 Beed
MAHARASHTRA
136
24 Latur123
25 Chandrapur120
26 Sangli117
27 Jalna104
28 Buldhana96
29 Yavatmal93
30 Bhandara80
31 Dhule69 JK89C#J"#E! N 2E#MIJ#K#J #E &E#8 ?E M#E:??8J 0L#K89#C#KP ?E8CPJ#J 46
S. No.DistrictState No of Colleges
32 Gondia69
33
Osmanabad/
Narmadapuram
67
34 Palghar66
35 Sindhudurg64
36 Pratapgarh
RAJASTHAN
185
37 Barmer80
38 Jalore71
39 Pali66
40 Dholpur57
41 Namakkal
TAMIL NADU
135
42 Erode94
43 Viluppuram83
44 Dharmapuri78
45 Tiruvannamalai75
46 Pudukkottai66
47 Thoothukkudi65
48 Khammam
TELANGANA
123
49 Adilabad94
50 Ghazipur
UTTAR PRADESH
325
51 Azamgarh276
52 Mau176
53 Sultanpur164
54 Ambedkar Nagar145
55 Deoria137
56 Hardoi133
57 Bijnor124
58 Etah116
59 Mainpuri104
60 Sitapur102
61 Gonda95
62 Unnao94
63 Jalaun93
64 Baghpat92 Establishing New Universities in India: An Evidence-based Suitability Analysis 47
S. No.DistrictState No of Colleges
65 Kannauj92
66 Kanpur Dehat89
67 Mirzapur86
68 Fatehpur85
69 Muzaffarnagar84
70 Kushinagar82
71 Farrukhabad81
72 Kaushambi80
73 Hathras79
74 Mahrajganj72
75 Auraiya70
76 Basti70
77 Sant Kabir Nagar70
78 Kheri66
79 Shahjahanpur60
80 Chandauli57
* In alphabetical order of state/UT names; District names are as per Integrated Government
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Annexure V: Districts as identified in the
Region-level Recommendations
S. No. DistrictState
Number of
College University
1 Changlang
ARUNACHAL PRADESH
2 0
2 East Siang41
3 Lohit41
4 Lower Siang0 0
5 Shi Yomi0 0
6 Siang1 0
7 Upper Siang1 0
8 Upper Subansiri1 0
9 West Kameng1 0
10 West Siang41
11 Dibrugarh
ASSAM
22 1
12 Nagaon39 1
13 Sonitpur22 1
14 Tinsukia17 0
15 Udalguri11 0
16 Jamui
BIHAR
11 0
17 Kaimur (Bhabua)17 0
18 Purbi Champaran23 1
19 Rohtas42 1
20 Balrampur
CHHATTISGARH
29 0
21 Bastar24 1
22 Bijapur6 0
23
Dakshin Bastar
Dantewada
11 0
24 Kabeerdham19 0
25 Kondagaon7 0
26 Korea21 0
27 Narayanpur4 0
28 Surajpur10 0
29
Uttar Bastar Kank-
er
8 0 Establishing New Universities in India: An Evidence-based Suitability Analysis 49
S. No. DistrictState
Number of
College University
30 DahodGUJARAT35 0
31 Doda
JAMMU AND KASHMIR
(UT)
8 0
32 Kishtwar6 0
33 Kulgam7 0
34 Poonch7 0
35 Ramban5 0
36 DeogharJHARKHAND17 1
37 Alirajpur
MADHYA PRADESH
9 0
38 Anuppur34 1
39 Barwani37 0
40 Jhabua16 0
41 Khandwa24 1
42 Singrauli32 0
43 Gadchiroli
MAHARASHTRA
77 1
44 Nanded121 1
45 Nandurbar46 0
46 Parbhani81 1
47 Chandel
MANIPUR
3 0
48 Churachandpur6 0
49 Kamjong0 0
50 Noney0 0
51 Senapati13 1
52 Tamenglong2 0
53 Tengnoupal1 0
54 West Garo Hills MEGHALAYA13 1
55 Longleng
NAGALAND
1 0
56 Mon2 0
57 Tuensang3 0
58 Zunheboto1 1
59 Balangir
ODISHA
32 1
60 Boudh7 0
61 Bhadrak41 0
62 BanswaraRAJASTHAN70 1 Establishing New Universities in India: An Evidence-Based Suitability Analysis 50
S. No. DistrictState
Number of
College University
63 VirudhunagarTAMIL NADU63 1
64 Karimnagar
TELANGANA
173 1
65 Mancherial2 0
66 Nirmal1 1
67 Dhalai
TRIPURA
4 0
68 Gomati1 0
69 North Tripura6 0
70 Auraiya
UTTAR PRADESH
70 0
71 Bahraich45 0
72 Ballia147 1
73 Farrukhabad81 0
74 Kheri66 0
75 Shrawasti11 0
76 Sonbhadra47 0
* In alphabetical order of state/UT names; District names are as per Integrated Government
Online Directory Establishing New Universities in India: An Evidence-based Suitability Analysis 51
Annexure VI: Composite score for States for
State-level Recommendations
RankState Name GER
Population ('000s)Per lakh population
Composite
IndexTotal
18-23
years
Unive-
rsity
College
1
Uttar
Pradesh
25.32,29,67225,289 0 32 5.22
2 Maharashtra32.31,23,96113,193 1 35 3.85
3
West
Bengal
19.997,871 10,833 1 14 3.74
4 Bihar 14.51,22,34112,023 0 11 3.70
5 Rajasthan 24.178,861 9,156 1 46 3.62
6
Madhya
Pradesh
24.284,040 9,021 1 41 3.48
7 Chhattisgarh52.11,250 205 17 465 3.41
8 Gujarat 21.369,403 7,246 1 39 3.26
9 Karnataka 32 66,627 6,843 1 66 3.13
10
Andaman
and Nicobar
Islands
20 348 51 0 18 3.12
11
Andhra
Pradesh
35.252,669 5,388 1 1 2.95
12 Chandigarh 18.528,492 3,170 0 1 2.94
13 Odisha 21.745,552 4,590 1 28 2.92
14 Assam 17.336,174 3,760 1 16 2.91
15 Ladakh 7.9 297 37 5 14 2.89
16 Jharkhand 20.938,274 3,909 1 9 2.88
17 Haryana 29.329,881 3,182 2 35 2.83
18 Tamil Nadu 51.476,255 6,853 1 41 2.83
19 Telangana 35.637,574 3,902 1 54 2.82
20
Dadra and
Nagar
Haveli and
Daman and
Diu
9.4 1,104 132 0 16 2.80
21 Delhi 48 20,414 2,359 1 8 2.75
22 Punjab 28.230,512 3,079 1 35 2.73
23 Goa 28.41,555 186 2 39 2.71 -.&#-"#(!1(#0,-#.#-#( (#?(0#(??-/#.#&#.3?(&3-#- 52
RankState Name GER
Population ('000s)Per lakh population
Composite
IndexTotal
18-23
years
Unive-
rsity
College
24 Lakshadweep7.5 68 7 0 40 2.65
25 Nagaland 26.12,182 241 2 29 2.59
26 Mizoram 26.11,162 127 2 31 2.57
27 Tripura 20.24,051 427 1 13 2.56
28 Puducherry 46.31,488 173 2 53 2.56
29
Arunachal
Pradesh
35.41,596 158 6 1692 2.54
30 Uttarakhand41.511,346 1,189 3 42 2.53
31 Meghalaya 26.13,272 335 3 24 2.49
32 Manipur 38.33,149 325 3 33 2.43
33 Kerala 38.834,127 2,934 1 53 2.43
34 Sikkim 75.8673 77 10 39 2.41
35
Jammu and
Kashmir
32.413,365 1,220 1 30 2.36
36
Himachal
Pradesh
40.87,374 709 4 54 2.36
* In descending order of composite score for state/UTs v
Designed by: